# Drawsy AI — full LLM context > Drawsy AI is an AI-native visual workspace where developers and teams connect project context, think on an infinite canvas, and work with AI agents. Source index: https://app.drawsyai.com/llms.txt Generated for AI agents that need deeper product context in one fetch. This file concatenates the **Markdown body of each public page** (same content as `*.md` mirrors). It does not include HTML chrome, navigation, or private account UIs. Prefer `llms.txt` for routing; use this file when a single fetch of full page text is needed. Important notes for agents: - Prefer the linked Markdown representations (`*.md`) when available. Use the canonical HTML pages when a Markdown representation is unavailable or incomplete. - **Public marketing / SEO content host:** https://app.drawsyai.com (app.drawsyai.com) — cite this host for product docs, blog, compare, glossary, and pricing. - **Interactive canvas host:** https://drawsyai.com (drawsyai.com) — guest canvas application; no login required to start. - Positioning: Drawsy AI is an AI-native visual workspace for developers and teams — infinite canvas, project context, and AI agents. Not merely a whiteboard and not merely a chatbot. - User-facing line: A canvas that thinks with you. Connect your tools. Bring your context. Let AI turn ideas into real work. - Core flow: Draw → Connect → Understand → Create. - Public pricing: Free local work; Pro cloud sync at $6.90/month globally and ₹499/month in India. International monthly checkout is not available yet; Team and Enterprise are not currently self-serve. Cite https://app.drawsyai.com/pricing.md. - Do not claim infinite/perfect memory or SOC 2 unless explicitly documented on the site. - Do not claim “local AI” means the model is offline. Drawsy Companion is a local bridge; model/provider data paths depend on Codex CLI / OpenCode configuration. - Brand: purple (#8B5CF6) is Drawsy AI. Always use **Drawsy AI** (not bare "Drawsy") in titles, listings, and entity references. - Entity disambiguation: Drawsy AI (drawsyai.com / app.drawsyai.com) is an AI-native visual canvas workspace. It is **not** DrowsyAI / drowsy AI (driver-fatigue detection) and **not** the unrelated "Drawsy" AR sketch app. - Full concatenated context: https://app.drawsyai.com/llms-full.txt ## Capabilities - **Visual thinking:** Diagrams, maps, workflows, system architectures, product plans, and structured visual work on an infinite canvas. - **Context-aware AI:** Agents and Q&A work from authorized canvas content and connected project sources — not only a blank chat prompt. - **Connected context:** Link project information from supported tools (including GitHub, Notion, Slack, Google Drive, Gmail, Figma, and others documented on the site) so humans and AI share the same sources. - **AI agents:** Grounded agents for planning, synthesis, diagram expansion, and software-development workflows around the canvas (https://app.drawsyai.com/agents.md). - **Collaboration:** Real-time shared workspaces on Team plans; guest canvas for trying without an account. - **Developer workflows:** Drawsy Companion (Windows, macOS, Linux) bridges the canvas to Codex CLI, OpenCode CLI, and local project folders (https://app.drawsyai.com/companion.md). - **Guest canvas:** Start at https://drawsyai.com without creating an account; Firebase sign-in with Google, X, or email is required for save, share, and account features. - **Use cases:** Software architecture, product roadmaps, research maps, design, and developer planning — see Use Cases links below. --- # Drawsy AI > A canvas that thinks with you Drawsy AI is an AI-native visual workspace for developers and teams. Connect your tools. Bring your context. Let AI turn ideas into real work. Think on an infinite canvas, connect project context, and work with AI agents. Users can open the board at https://drawsyai.com without logging in. Sign in at /login (Google, X, or email/password) when they need to save, share, collaborate, or unlock account features. ## What Drawsy AI is Drawsy AI is an AI-native visual workspace for developers and teams. Connect your tools. Bring your context. Let AI turn ideas into real work. It is a place to think on an infinite canvas, connect project context, and work with AI agents — not merely a whiteboard and not merely a chatbot. ## Core flow **Draw → Connect → Understand → Create** 1. **Draw** — sketch ideas, diagrams, systems and plans on an infinite canvas 2. **Connect** — bring documents, code, meetings, conversations and tools into the workspace 3. **Understand** — ask Drawsy using relevant project context (canvas + connected sources + prior decisions) 4. **Create** — turn ideas into plans, documents, diagrams and decisions ## Primary entry points - Website / landing: https://app.drawsyai.com (app.drawsyai.com) - Free canvas: https://drawsyai.com (drawsyai.com — guest canvas) - Login: /login (drawsyai.com/login) - Profile: /profile - App pricing (authenticated): /pricing - Billing: /billing - Public marketing pricing: https://app.drawsyai.com/pricing ## User flow (authoritative) 1. Visit https://app.drawsyai.com 2. Click **Start building free** 3. Land on https://drawsyai.com and use the canvas with **no login** 4. Get value (sketch, connect context, collaborate, ask AI) 5. When the user wants to **save**, **share**, **continue across devices**, or use **account features**, sign in at /login 6. New users get an account automatically and enter the workspace — no workspace picker; email accounts verify their address first ## Key product surfaces - Infinite collaborative canvas (https://app.drawsyai.com/#product) - How it works (https://app.drawsyai.com/#how-it-works) - What you can build (https://app.drawsyai.com/#what-you-can-build) - Use cases (https://app.drawsyai.com/#use-cases) - Connected context (https://app.drawsyai.com/#connectors) - Agents that run with canvas + connected context (https://app.drawsyai.com/agents) - Drawsy Companion for local tools (https://app.drawsyai.com/#companion) - Real-time collaboration ## What you can build > One canvas. Many ways to think. Drawsy is one AI canvas for the work teams actually do. Featured workflows (with in-page demos): | Workflow | Typical work | Landing anchor | | --- | --- | --- | | System Architecture | Map services, APIs, and infrastructure | https://app.drawsyai.com/#system-architecture | | Product Roadmap | Plan milestones, priorities, and delivery timelines | https://app.drawsyai.com/#product-roadmap | | Create Design | Wireframes, UI, and visual thinking | https://app.drawsyai.com/#create-design | | Research | Collect notes and turn insights into understanding | https://app.drawsyai.com/#research-maps | Section: https://app.drawsyai.com/#what-you-can-build ## More ways to explore > More ways to explore. | Category | Typical work | Why Drawsy | | --- | --- | --- | | Systems | Flows, architecture, APIs | Architecture maps stay linked to code/docs context | | Knowledge | Notes, research, docs | Structure knowledge for reuse | | Research | Sources, evidence and insights | Connected sources reduce context rebuild | | Planning | Roadmaps, tasks, trackers | Plans stay beside research and decisions | | Design | Wireframes, UI, visual thinking | Move from sketch to artifact with AI help | | Apps | Code, prototypes, live previews | Build beside the context that informs it | | Present | Slides, stories, reports | Turn the board into something you can share | Landing anchors: https://app.drawsyai.com/#systems · https://app.drawsyai.com/#knowledge · https://app.drawsyai.com/#research · https://app.drawsyai.com/#planning · https://app.drawsyai.com/#design · https://app.drawsyai.com/#apps · https://app.drawsyai.com/#present Legacy aliases still resolve: /#build · /#learn · /#plan · /#create · /#organize · /#decide ## Connectors > Your work already lives everywhere. Drawsy connects it. Connectors bring documents, code, conversations, meetings and data into the workspace so Drawsy can work with the context that matters. - **HydraDB** — structured project data (https://app.drawsyai.com/#hydradb) - **Gmail** — email and threads (https://app.drawsyai.com/#gmail) - **Calendar** — meetings and schedules (https://app.drawsyai.com/#calendar) - **Notion** — docs and knowledge (https://app.drawsyai.com/#notion) - **Google Drive** — files and documents (https://app.drawsyai.com/#google-drive) - **Slack** — team conversations (https://app.drawsyai.com/#slack) - **GitHub** — code and repositories (https://app.drawsyai.com/#github) - **Read AI** — meeting notes (https://app.drawsyai.com/#read-ai) - **Fireflies** — call transcripts (https://app.drawsyai.com/#fireflies) - **AWS** — cloud context for systems work (https://app.drawsyai.com/#aws) You control what Drawsy can access. Context stays scoped to your work. Basic connectors are available on Free; all connectors unlock on Pro+ (see Pricing). ## Related pages - [How to start](https://app.drawsyai.com/how-to.md) - [Pricing](https://app.drawsyai.com/pricing.md) - [Compare](https://app.drawsyai.com/compare.md) - [Agents](https://app.drawsyai.com/agents.md) - [About](https://app.drawsyai.com/about.md) --- # How to start with Drawsy AI > Use first. Sign up later. ## Steps 1. Go to https://app.drawsyai.com 2. Choose **Start building free** (opens https://drawsyai.com) 3. Start drawing and organizing ideas in **guest mode** — no account wall 4. When you need to save, share, collaborate, or keep work across devices, open /login and choose Google, X, or email/password 5. New users are created automatically; email accounts verify their address before entering the workspace 6. Return to the canvas with unlocked account features ## Why this flow Drawsy removes authentication friction until the user has experienced value and needs persistence or collaboration. --- # Drawsy AI Agents > Grounded in the work — not a separate chat silo. Drawsy AI Agents operate with canvas objects, connected sources and prior decisions. They help research, expand structure, analyze options and move work forward while staying scoped to relevant project context. ## What agents are for - Expanding diagrams and plans from partial sketches - Asking questions that require canvas + source context - Summarizing decisions and open questions - Generating next-step structure without leaving the board ## Limits Agent runs vary by plan (see Pricing). Do not invent unlimited agent capacity unless the live plan says so. --- # Drawsy Companion > Your canvas. Your machine. Drawsy Companion is a free desktop app that connects Drawsy Canvas to the coding tools already on your computer — including Codex CLI, OpenCode CLI, and local project files. The Companion bridge runs on your machine. **No Drawsy subscription is required.** Whether prompts or source reach an external model still depends on the coding agent and model provider you configure — local bridge ≠ offline model. Guide: [How to use a local AI coding agent with an AI canvas](https://app.drawsyai.com/blog/local-ai-coding-agent-ai-canvas.md) ## Download Windows, macOS, and Linux — https://app.drawsyai.com/companion#download Latest release: see https://app.drawsyai.com/companion for installers and portable builds. ## How it works 1. Drawsy Canvas in your browser connects to Drawsy Companion on your computer over a secure local-only link. 2. Companion bridges to Codex CLI or OpenCode CLI (you choose one). 3. Your local project folder stays on your PC; Companion does not replace your agent’s model/provider configuration. Flow diagram: https://app.drawsyai.com/companion#how-it-works ## Setup (6 steps) 1. Download Companion for your OS 2. Install Codex CLI or OpenCode CLI 3. Start Companion (runs in the background) 4. Open Drawsy Canvas at https://drawsyai.com 5. Connect a local project folder 6. Start building with your local AI tools Full guide: https://app.drawsyai.com/companion#setup ## Platforms Windows, macOS, and Linux. ## Related - Landing section: https://app.drawsyai.com/#companion - Canvas: https://drawsyai.com - Agents: https://app.drawsyai.com/agents --- # Drawsy AI Pricing > Start free. Upgrade when it matters. ## Plans (authoritative) | Plan | Price | Intent | | --- | --- | --- | | Free | $0 forever (monthly) | Explore Drawsy | | Pro | $6.90 (monthly) | For individuals building more. | | Team | Coming soon (monthly) | For collaborative teams. | | Enterprise | Custom | For organizations that need more control. | Pro is $6.90 per month globally and ₹499 per month in India. International monthly checkout is disabled until PayU confirms a supported mandate for this merchant. ## Feature matrix | Feature | Free | Pro | Team | Enterprise | | --- | --- | --- | --- | --- | | Media storage | This device | 10 GB cloud | Planned | Custom | | Presentation sync | — | ✓ | Planned | Custom | | Kanban sync | — | ✓ | Planned | Custom | | Kanban invitations | Local import | Live boards | Planned | Custom | | Cloud comments | — | ✓ | Planned | Custom | ## Principles - Free / guest access to the canvas at https://drawsyai.com - Sign in at /login for account features - No credit card required to start - Cancel anytime on paid plans Source of truth for live UI: https://app.drawsyai.com/pricing --- # Drawsy AI Alternatives & Comparisons > Compare Drawsy AI with visual collaboration, diagramming, and technical workspace tools to find the right fit for your workflow. There is no universally best tool — start with the job. ## Start with the job - Collaborative whiteboarding → Miro / FigJam - Fast visual diagramming → Excalidraw - Technical architecture → Eraser / Drawsy AI - Context + AI + development → Drawsy AI ## Comparisons - [Drawsy AI vs Miro](https://app.drawsyai.com/compare/miro.md) — broad collaborative whiteboarding and workshops - [Drawsy AI vs FigJam](https://app.drawsyai.com/compare/figjam.md) — product/design collaboration in the Figma ecosystem - [Drawsy AI vs Excalidraw](https://app.drawsyai.com/compare/excalidraw.md) — lightweight hand-drawn diagrams and sketches - [Drawsy AI vs Eraser](https://app.drawsyai.com/compare/eraser.md) — technical diagramming and architecture workflows ## How Drawsy AI is different ```text Project ↓ Canvas + Connected context ↓ AI ↓ Agents ↓ Code ↓ Working software ``` Drawsy AI is built around the relationship between visual work, project context, AI, and development workflows. It is not trying to replace every whiteboard, diagramming tool, or project-management system. Canonical HTML: https://app.drawsyai.com/compare --- # Drawsy AI vs Miro > Two visual workspaces, different starting points: Miro is built around collaborative whiteboarding and innovation workflows; Drawsy AI is built around visual project context, connected sources, and AI-assisted development workflows. ## Quick comparison Both products put teams on an infinite canvas with AI and integrations. The useful question is not “which whiteboard wins every feature,” but which center of gravity matches your work. | | Drawsy AI | Miro | | --- | --- | --- | | Primary model | Visual project workspace | Collaborative innovation workspace | | Canvas | Infinite canvas | Infinite canvas | | AI | AI + agents around project context | Miro AI / Sidekicks / Flows | | External context | Connected project sources | Integrations + connected content | | Developer workflow | Strong focus | Broad / general-purpose | | Architecture | Architecture + project context | Diagramming + architecture | | Coding agents | Companion / supported coding workflows | MCP + coding-tool workflows | | Live software workflow | Canvas → agent → running project | Canvas → agentic workflows | | Collaboration | Shared visual workspace | Deep real-time collaboration | | Best fit | Developer / product / technical workflows | Broad cross-functional teams | ## Different centers of gravity Miro and Drawsy AI solve overlapping visual-work problems, but they organize the workflow around different centers of gravity. Miro’s strength is collaborative whiteboarding and innovation work at scale — workshops, facilitation, templates, diagramming, AI assistance, and increasingly agentic workflows across teams. Drawsy AI’s strength is keeping visual project context next to the sources and agents that developers and technical teams actually use — so plans do not stop at the board. ## Workflow comparison Feature lists blur together as both products add AI. Workflow shape is the clearer signal. ```text Miro Idea ↓ Canvas ↓ Collaborate ↓ AI ↓ Plan / communicate ``` ```text Drawsy AI Project ↓ Canvas + Connected context ↓ AI ↓ Agent ↓ Code ↓ Running software ``` ## Where the differentiation actually is Miro now advertises AI diagramming, cloud architecture capabilities, MCP, and agentic coding workflows. It is not honest — or useful — to pretend “AI + canvas + agents” belongs only to Drawsy AI. Drawsy AI’s differentiation is how deeply the visual project context is tied to the developer workflow: repository and project sources beside the canvas, agents that work from that context, and a path from architecture toward implementation and running software. That is a narrower claim than “better AI whiteboard,” and a stronger one for technical teams. ## Miro is better when… Choose Miro when collaborative breadth and facilitation are the main job. - Large-scale collaborative workshops and innovation sessions - Broad enterprise collaboration across many functions - Extensive template libraries and mature facilitation features - General-purpose whiteboarding as the primary need - Deep integration ecosystems already standardized on Miro ## Drawsy AI is better when… Choose Drawsy AI when the board has to stay close to technical work. - The work is highly technical and architecture is central - The repository and project context matter beside the diagram - Connected sources need to sit on the same surface as the plan - AI coding agents are part of how the team ships - You want a path from architecture → implementation → running software ## Architecture: both can diagram — surroundings differ Do not choose based on “Miro can’t do architecture.” Miro supports software architecture diagrams, templates, collaboration, AI-assisted diagramming, and cloud architecture workflows. Both can support architecture work. The difference is what surrounds the diagram. ```text Miro Architecture diagram + Collaboration + AI + Templates Drawsy AI Architecture + Repository + Project context + Connected sources + Coding agent + Running software ``` - See how Drawsy AI handles system architecture and AWS infrastructure on [AI System Architecture Tool](/use-cases/system-architecture). - For developer-centered workflows around the same idea, see [An AI Workspace for Developers](/use-cases/developers). ## Miro vs Drawsy AI for developers Developers often land on Miro for architecture sketches, system maps, or planning boards — then hit a gap when the diagram needs to stay linked to the repo, tickets, and coding agents. If you are searching for a Miro alternative for developers, or comparing Miro vs Drawsy AI for technical work, the decision usually turns on that gap. For architecture workflows in depth, see [AI System Architecture Tool](/use-cases/system-architecture). Miro can still be the right board for cross-functional workshops that include engineers. Drawsy AI is the better primary workspace when the canvas is part of how the team designs and ships software — not only how they facilitate meetings. - Architecture and system maps that stay next to project context - Connected sources (code, docs, chat, cloud) authorized per prompt - Companion-supported coding workflows beside the board - Guest-first access to try the canvas before account setup ## AI, MCP, and agentic workflows — honest overlap Both products are moving into AI that acts with context, not only chat on a board. Miro’s direction includes Miro AI, Sidekicks, Flows, MCP, and agentic paths that can involve coding tools. Drawsy AI focuses AI and agents on canvas state plus connected project sources, with Companion bridging local coding workflows. Overlap is real; the product bet is different: Miro optimizes for collaborative innovation surfaces at scale, Drawsy AI for visual project memory inside technical delivery. ## Collaboration Miro has long-standing depth in real-time collaboration, facilitation, and enterprise teamwork. That remains a clear Miro strength. Drawsy AI offers real-time collaboration on Team plans, with permissions and roles for shared workspaces. Free and Pro users can share exports; persistent team collaboration is a Team-tier feature. See [Pricing](/pricing) for details. ## Using both Many teams keep Miro for facilitated workshops and use Drawsy AI for architecture, planning, and decisions that need connected context over weeks — not a single session. Start free at https://drawsyai.com. Recreate critical technical boards, connect sources, and sign in at /login when you need save and share. ## What Drawsy AI is not claiming Drawsy AI is not trying to replace every Miro template, facilitation feature, or enterprise whiteboard rollout. It is not an exclusive owner of AI diagramming or agentic canvas workflows. It is a better fit when visual work must stay tied to project context and developer delivery — and a weaker fit when your primary need is workshop-scale collaboration tooling. Start free: https://drawsyai.com ## FAQ ### Is Drawsy AI a Miro alternative? Yes, for teams whose primary need is a visual workspace tied to project context, repositories, and AI-assisted development — not for every Miro use case. Miro remains stronger for large workshops, facilitation, and broad cross-functional whiteboarding. ### Can Miro do software architecture? Yes. Miro offers diagramming, architecture templates, AI-assisted diagrams, and cloud architecture workflows. The difference is what surrounds the diagram: Drawsy AI keeps architecture next to repository context, connected sources, coding agents, and a path toward running software. ### Does Miro support AI and coding agents? Miro markets Miro AI, Sidekicks, Flows, MCP, and agentic workflows that can reach coding tools. Drawsy AI’s differentiation is how deeply visual project context sits inside a developer-centered loop — canvas, connected sources, Companion-supported coding workflows, and live project work — not exclusive ownership of “AI on a canvas.” ### When should developers choose Miro instead of Drawsy AI? Choose Miro when you need mature facilitation, extensive templates, enterprise collaboration patterns, or a general-purpose innovation board for large cross-functional sessions. Choose Drawsy AI when architecture, repository context, and AI coding workflows are central to the work. ### Can we use Miro and Drawsy AI together? Many teams keep Miro for workshops and facilitation, then move durable technical plans into Drawsy AI when the board needs to stay linked to code, docs, and agents over time. ### Is Drawsy AI better for Miro architecture alternatives? If you are using Miro for architecture sketches and want the diagram to stay tied to project context and implementation, Drawsy AI is often the closer fit. For that workflow in depth, see the AI System Architecture Tool use case — this page owns the Miro comparison, not the broad architecture topic. --- # Drawsy AI vs FigJam > Two visual collaboration tools, different centers of gravity: FigJam is built for product and design collaboration inside the Figma ecosystem; Drawsy AI is built around visual project context, connected sources, and AI-assisted workflows that continue into build. ## Quick comparison Both help teams think visually together. The useful question is whether your center of gravity is product/design collaboration in Figma — or a project workspace that stays connected through research, planning, and build. | | Drawsy AI | FigJam | | --- | --- | --- | | Primary model | Visual project workspace | Product/design collaboration board | | Canvas | Infinite canvas | Infinite canvas | | Ecosystem | Cross-tool project sources | Figma ecosystem | | AI | AI + agents around project context | FigJam AI for board collaboration | | Best collaboration fit | Product + engineering + research | Design + product workshops | | Planning depth | Roadmaps, architecture, delivery context | Ideation, critiques, lightweight plans | | Build path | Context → agents → implementation | Workshop → design/product handoff | | Best fit | Technical and cross-functional project work | Figma-native product/design teams | ## Where they overlap FigJam and Drawsy AI both support visual collaboration: sticky-style ideation, spatial planning, real-time teamwork, and AI assistance on a board. Overlap is real. The difference is what the board is for after the workshop ends — and how far the workflow continues past design and product collaboration. ## Different workflow shapes Feature checklists blur together. Workflow shape is the clearer signal. ```text FigJam Brainstorm ↓ Collaborate ↓ Design / Product ↓ Plan ``` ```text Drawsy AI Project ↓ Visual context ↓ Connected sources ↓ AI ↓ Agents ↓ Build ``` ## FigJam is better when… Choose FigJam when collaboration inside the Figma ecosystem is the main job. - Design-led workshops, critiques, and brainstorming sessions - Teams already standardized on Figma + FigJam - Sticky-note facilitation, voting, and lightweight visual alignment - Product/design collaboration that hands off into Figma files - You primarily need a collaborative whiteboard — not a project context workspace ## Drawsy AI is better when… Choose Drawsy AI when visual work must stay tied to the wider project. - Research, requirements, and decisions need to live beside the plan - Product and engineering share one visual context graph - Connected sources (docs, code, chat, design) matter on the same board - AI should reason over project context — not only workshop notes - The workflow continues toward agents, implementation, or durable roadmaps ## Product and design collaboration Do not dismiss FigJam as “just a whiteboard.” It is a strong product/design collaboration surface — especially for teams that live in Figma. Drawsy AI is not trying to win Figma-native workshop facilitation. Its product-side strength is carrying visual work forward: from research and planning into engineering-aware context, without stranding outcomes in a session board. ## Research → roadmap → engineering Product teams often start in FigJam for discovery and alignment, then lose the thread when research, roadmap priorities, and engineering constraints scatter across tools. Drawsy AI is built for that handoff surface: keep research evidence, priorities, and technical context on one canvas so planning does not stop at sticky clusters. For the product planning workflow specifically, see [AI Product Roadmap Tool](/use-cases/product-roadmap). For engineering continuity, see [An AI Workspace for Developers](/use-cases/developers). ## AI + project context FigJam AI helps inside collaborative board workflows and the Figma ecosystem. That is a real strength for design/product sessions. Drawsy AI’s AI and agents are oriented around canvas state plus connected project sources you authorize — so a planning question can reference research notes, a Notion PRD, Slack decisions, and related code context on the same workspace. Neither replaces product judgment. The difference is which context the AI can assemble when the work spans functions. ## Where Drawsy AI fits for technical teams If your FigJam board is mainly workshops and design collaboration, stay with FigJam — or use both. If the same initiative needs architecture maps, repository context, coding agents, or a path from plan to build, Drawsy AI becomes the stronger primary workspace for that technical layer. Architecture-heavy work belongs on [AI System Architecture Tool](/use-cases/system-architecture) — not forced into a FigJam comparison. ## Using both A common pattern: FigJam for design workshops and early product alignment; Drawsy AI for durable project boards that connect research, roadmap, and engineering context. Start free at https://drawsyai.com. Recreate the plans that need to persist, connect sources (including Figma), and sign in at /login when you need save and share. ## What Drawsy AI is not claiming Drawsy AI is not a Figma replacement and is not trying to out-facilitate FigJam inside design org rituals. It is not “FigJam plus AI” as a feature checklist. It is a better fit when visual collaboration must stay connected to project context and build workflows — and a weaker fit when your primary need is Figma-native workshop tooling. Start free: https://drawsyai.com ## FAQ ### Is Drawsy AI a FigJam alternative? Yes, for teams whose visual work must stay connected to research, product plans, engineering context, and AI agents — not for every FigJam use case. FigJam remains stronger for Figma-native workshops, sticky-note brainstorming, and design-led facilitation. ### When should product teams choose FigJam? Choose FigJam when collaboration lives inside Figma, workshops are design-led, and the primary job is brainstorming, critiquing, or aligning visually with design teammates. Choose Drawsy AI when the board needs to carry project context into roadmaps, architecture, and delivery. ### Can FigJam and Drawsy AI be used together? Yes. Many teams run FigJam for design workshops, then move durable product and technical plans into Drawsy AI when research, requirements, and engineering sources need to sit beside the visual plan over time. ### Does Drawsy AI replace Figma or FigJam? No. Drawsy AI is not a design tool or a Figma replacement. It can connect Figma alongside other project sources so product and engineering work stay linked to design context — without trying to own the Figma ecosystem. ### Is Drawsy AI better for product roadmaps than FigJam? If the roadmap needs research evidence, priorities, and a path toward engineering execution on the same workspace, Drawsy AI is usually the closer fit. FigJam is often better for early collaborative ideation before the plan needs durable project context. ### How does AI differ between FigJam and Drawsy AI? FigJam AI is oriented toward board and Figma-ecosystem collaboration. Drawsy AI assembles answers from canvas regions plus connected project sources you authorize — useful when a product question spans research, docs, code, and the visual plan. --- # Drawsy AI vs Excalidraw > Two visual tools, different jobs for the diagram: Excalidraw is built for lightweight, fast, hand-drawn diagramming; Drawsy AI is built for situations where the diagram is part of a larger project workflow — with connected sources, AI, agents, and a path toward implementation. ## Quick comparison The central question is not “which canvas has more features.” It is: do you need a simple diagram, or is the diagram part of a larger project workflow? | | Drawsy AI | Excalidraw | | --- | --- | --- | | Core focus | Visual project workspace | Lightweight visual diagramming | | Canvas | Infinite visual workspace | Infinite canvas | | Drawing style | Flexible visual workspace | Hand-drawn / sketch-like | | Project context | Connected sources + canvas | Primarily diagram / canvas workflow | | AI | AI + agents around project context | Generative diagram AI (esp. Excalidraw+) | | Developer workflow | Architecture → context → agents → build | Diagramming / technical visualization | | Collaboration | Project workspace | Collaborative canvas | | Best fit | Context-heavy technical / product work | Fast, simple diagrams and sketches | ## What both are good at Both put ideas on an infinite canvas quickly. Both work well for technical sketches, system maps, and visual explanation. Both support collaborative editing in their ecosystems. Excalidraw’s strength is staying light: open, sketch, share — with a hand-drawn feel that lowers the cost of thinking out loud. Drawsy AI keeps a visual-first surface while organizing the diagram as part of ongoing project work. ## The diagram has a different role Feature lists blur as both products add AI and collaboration. Workflow role is the clearer signal. ```text Excalidraw Idea ↓ Sketch ↓ Diagram ↓ Share ``` ```text Drawsy AI Project ↓ Context ↓ Canvas ↓ AI ↓ Agent ↓ Code ↓ Running software ``` ## Excalidraw is better when… Choose Excalidraw when simplicity is the point. - You want to sketch something quickly and move on - You prefer a hand-drawn visual style that stays low-friction - You need a lightweight diagramming experience without a broader project workspace - The diagram is the deliverable — not a hub for ongoing project context - You want open-source / minimal-setup diagramming for personal or ephemeral work ## Drawsy AI is better when… Choose Drawsy AI when the sketch has to stay connected to the work around it. - The diagram is part of an ongoing project, not a one-off file - Architecture needs to sit beside code and project context - Connected sources matter on the same board as the drawing - AI should reason over relevant project context, not only the scene - Coding agents are part of how the team ships - You want a path from architecture toward implementation and running software ## For developers: diagrams or development context? Excalidraw is excellent when the primary task is drawing — a quick service map, a whiteboard sketch in a meeting, a diagram for a PR or doc. Drawsy AI is designed for situations where the diagram is one artifact inside a larger technical workflow: repository context, requirements, infrastructure, agents, and implementation sitting around the same visual layer. If you are evaluating an Excalidraw alternative for developers or architecture work, start with [AI System Architecture Tool](/use-cases/system-architecture) and [An AI Workspace for Developers](/use-cases/developers) — those pages own the workflow depth; this page owns the comparison. ## From diagram to implementation When a sketch becomes the living map of a system, Drawsy AI’s role is to keep that map inside the development loop — not only to export a prettier PNG. ![Architecture and project context on a Drawsy AI canvas — from diagram toward implementation](https://app.drawsyai.com/assets/use-cases/system-architecture.jpg) ```text Diagram ↓ Architecture ↓ Project context ↓ AI ↓ Coding agent ↓ Implementation ``` - Architecture stays editable beside connected project sources - AI and agents can work from selected canvas + authorized context - Companion bridges supported local coding workflows when you are ready to build ## AI without overselling Excalidraw’s product line includes generative features such as text-to-diagram and related AI tooling (especially on Excalidraw+), plus agent-oriented integrations such as MCP in its hosted offering. Do not choose Drawsy AI because “Excalidraw has no AI.” Choose based on center of gravity: Excalidraw optimizes for diagram generation and lightweight visual work; Drawsy AI optimizes for diagrams that stay tied to project context and development workflows. For the broader canvas category — not this comparison — see [What is an AI canvas?](/ai-canvas). ## Context and connected sources Excalidraw workflows are primarily about the scene you draw and share. That is enough for many technical communication jobs. Drawsy AI adds connected project sources beside the canvas so [connected context](/connected-context) can inform questions, plans, and agent work — useful when the diagram must stay honest to the repo, docs, and decisions around it. Learn the practice behind that approach in [context engineering](/context-engineering) and [AI coding agents](/ai-coding-agents) when the next step is implementation. ## When Excalidraw is still the better choice Keep Excalidraw when you want maximum simplicity: a quick sketch, a hand-drawn illustration, a lightweight embed, or a diagram that does not need a project workspace wrapped around it. Drawsy AI is not trying to replace every fast Excalidraw habit. It is the better primary tool when visual work must compound into durable technical context — and a weaker fit when you only need to draw and share. ## Using both A common pattern: Excalidraw for quick personal sketches; Drawsy AI when a diagram becomes a shared system map that needs sources, AI, and a path toward build. Start free at https://drawsyai.com. Rebuild the diagrams that need to persist, connect sources, and sign in at /login when you need save and share. ## What Drawsy AI is not claiming Drawsy AI is not claiming Excalidraw lacks AI, MCP, collaboration, or diagram generation. Those capabilities exist in Excalidraw’s product line — especially on Excalidraw+. Drawsy AI is a better fit when the diagram must live inside a project → context → agent → software loop — and a weaker fit when you only need fast, lightweight sketching. Start free: https://drawsyai.com ## FAQ ### What is the difference between Drawsy AI and Excalidraw? Excalidraw centers on fast sketching and lightweight diagrams. Drawsy AI centers on visual project context: the diagram sits beside connected sources, AI, and coding workflows so technical work can continue past the sketch. ### Is Drawsy AI an Excalidraw alternative? Yes, when you need more than a diagram file — especially for developers and teams whose architecture, research, or plans must stay linked to project context. For quick standalone sketches, Excalidraw may still be the better choice. ### Is Excalidraw better for simple diagrams? Often yes. If the primary task is drawing something quickly in a hand-drawn style and sharing it, Excalidraw’s simplicity is a genuine strength. Drawsy AI is a better fit when the diagram must live inside an ongoing project. ### Can Drawsy AI be used for software architecture? Yes. Drawsy AI is used as an AI system architecture tool — mapping systems with project context, infrastructure, and a path toward agents and implementation. See the system architecture use case for that workflow. ### Can developers connect project context to Drawsy AI? Yes. Connectors bring authorized project sources onto the same canvas as the diagram so AI and teammates work from shared visual + source context. ### Can AI and coding agents work with Drawsy AI diagrams? Yes. Drawsy AI’s AI and agents can work from canvas state plus connected context, and Companion bridges supported local coding workflows. Excalidraw also offers generative AI and agent-oriented integrations in its product line — the difference is Drawsy AI’s center of gravity around project context and development workflow, not exclusive ownership of diagram AI. ### Which should I use: Excalidraw or Drawsy AI? Use Excalidraw when you need a simple diagram fast. Use Drawsy AI when the diagram is one artifact inside architecture, planning, or development work that needs connected context and a path toward implementation. Many people use both. --- # Drawsy AI vs Eraser > Two tools relevant to technical architecture — different jobs for the diagram: Eraser is built for AI-assisted technical diagramming and engineering documentation; Drawsy AI is built for a broader project workspace where architecture stays connected to context, AI, coding agents, and a path toward implementation. ## Where Drawsy AI and Eraser differ The central question is not which product “wins architecture.” It is: do you need a technical diagramming tool, or a broader project workspace connecting architecture to implementation? Eraser and Drawsy AI both matter to developers and technical teams. Their centers of gravity differ. ## Where they overlap Both can be relevant for technical diagrams, software architecture, developer workflows, documentation adjacent to systems, and AI-assisted technical work. Eraser is especially strong at turning prompts, code, and docs into structured diagrams — including cloud architecture, sequence diagrams, ERDs, and related technical visuals — with MCP and agent integrations for coding assistants. Drawsy AI overlaps on architecture visualization and AI, then continues into connected project sources, Companion-supported coding workflows, and a canvas → agent → code → running software loop. ## Quick comparison Focus on workflow role. Both products keep adding AI and agent surfaces — so “who has AI” is not a useful differentiator. | | Drawsy AI | Eraser | | --- | --- | --- | | Core focus | Visual project workspace | Technical diagramming + engineering docs | | Primary artifact | Canvas + project context | Diagrams / docs (incl. diagram-as-code) | | AI | AI + agents around project context | AI diagram generation / editing | | Coding agents | Companion + canvas workflow | MCP / agent integrations for diagrams | | Architecture | Architecture + context + implementation path | Architecture diagrams + technical docs | | Cloud / AWS diagrams | Infrastructure context on the canvas | Strong cloud architecture diagramming | | Project sources | Connected sources on the workspace | Code/docs/files as diagram inputs | | Best fit | Context-heavy architecture → build workflows | Focused technical diagramming & docs | ## Different workflow shapes The diagram plays a different role in each product. ```text Eraser Code / docs ↓ Diagram ↓ Technical documentation ↓ Collaborate ``` ```text Drawsy AI Project ↓ Context ↓ Canvas ↓ Architecture ↓ AI ↓ Agent ↓ Code ↓ Running software ``` ## Eraser is better when… Choose Eraser when technical diagramming and documentation are the main job. - You want a focused architecture / diagramming workflow - Technical documentation next to diagrams is the primary deliverable - You prefer diagram-as-code / structured technical visuals as the center of the product - Your team already has separate tools for broader project context and implementation - Coding agents should generate or update diagrams in a dedicated diagramming workspace ## Drawsy AI is better when… Choose Drawsy AI when architecture is one layer inside a larger project workspace. - The canvas is part of ongoing project work — not only a diagram file - Architecture needs to stay connected to project context and sources - AI should reason over canvas + authorized project context together - Coding agents are part of how the team ships from the same visual layer - You want a path from architecture toward implementation and running software ## Architecture workflow Both can support software architecture. Do not choose based on “Eraser can’t do architecture” or “Drawsy AI can’t diagram.” If you need Drawsy AI’s full architecture story — existing systems, AWS infrastructure maps, DRAW.md, agents, and live previews — use [AI System Architecture Tool](/use-cases/system-architecture). This comparison page owns the Eraser decision; that page owns the architecture topic. ## Code and project context Eraser can take code, docs, and related inputs into strong technical diagrams — including referencing repositories and files as diagram inputs. That is a real strength for engineering documentation. Drawsy AI connects project sources onto a shared visual workspace so architecture, decisions, and AI share [connected context](/connected-context) on the canvas: ```text GitHub ↓ Canvas ↓ Context ↓ Architecture ``` ## Architecture → implementation When the next step after the diagram is building, Drawsy AI’s differentiation is continuing on the same workspace — not exporting a diagram into a disconnected coding chat. ![Architecture on a Drawsy AI canvas connected to project context toward implementation](https://app.drawsyai.com/assets/use-cases/system-architecture.jpg) ```text Architecture ↓ Agent ↓ Code ↓ Run ↓ Live preview ``` - Learn the agent layer in [AI coding agents](/ai-coding-agents) - See the developer workflow in [An AI Workspace for Developers](/use-cases/developers) ## Keep architecture close to the repository DRAW.md is Drawsy AI’s bridge for keeping architecture close to the code — a repository-side document that can render into editable canvas content and stay available to coding agents. It is a differentiator for project-connected architecture work, not a claim that Eraser lacks documentation. For the full explanation, see the DRAW.md section on [AI System Architecture Tool](/use-cases/system-architecture). ```text Repository ↕ DRAW.md ↕ Drawsy AI canvas ↕ Coding agent ``` ## Which should I choose? Choose Eraser if technical diagramming and engineering documentation are the primary job — especially when you want AI/MCP-driven diagram generation as the product center. Choose Drawsy AI if your work combines architecture with project context, AI, coding agents, and a path toward implementation on one visual workspace. Use both when Eraser owns polished technical diagrams/docs and Drawsy AI owns the living project board around build. Start free at https://drawsyai.com. Sign in at /login when you need boards to persist. ## What Drawsy AI is not claiming Drawsy AI is not claiming to be “Eraser with more AI.” Eraser’s AI diagramming, cloud architecture visuals, docs workflow, and MCP agent integrations are real. Drawsy AI is a better fit when the diagram must live inside a broader project → context → agent → software loop — and a weaker fit when you only need a focused technical diagramming/docs tool. Start free: https://drawsyai.com ## FAQ ### What is the difference between Drawsy AI and Eraser? Eraser centers on technical diagrams and documentation — prompt-to-diagram, diagram-as-code, architecture visuals, and agent integrations that generate or update diagrams. Drawsy AI centers on a visual project workspace: architecture sits beside connected project context, AI, coding agents, and a path toward running software. ### Is Drawsy AI an Eraser alternative? Yes, when you need more than a focused diagramming/docs workflow — especially if architecture must stay tied to project sources, agents, and implementation. If your primary job is producing and maintaining technical diagrams and docs, Eraser may still be the better fit. ### Does Eraser support AI and coding agents? Yes. Eraser offers AI diagram generation and editing, and MCP / agent integrations so coding agents can create and update diagrams in an Eraser workspace. Drawsy AI also works with AI and coding agents — the difference is Drawsy AI’s center of gravity around canvas + project context + implementation, not exclusive ownership of diagram AI. ### Which is better for software architecture? Both can support architecture work. Eraser is strong when the deliverable is a technical diagram or architecture doc. Drawsy AI is stronger when the architecture map must stay connected to repository context, infrastructure context, coding agents, and a path toward build. For that workflow in depth, see the AI System Architecture Tool use case. ### Can Drawsy AI visualize AWS or cloud infrastructure? Yes — as part of the system architecture workspace, with AWS resource context brought onto an editable canvas. Eraser also has strong cloud architecture diagramming. Choose based on whether you need a focused diagramming tool or a broader project workspace. ### What is DRAW.md in Drawsy AI? DRAW.md keeps architecture close to the repository — a project document that can render into editable canvas content and stay available to coding agents. It is detailed on the system architecture page rather than fully restated here. ### Which should I choose: Eraser or Drawsy AI? Choose Eraser when technical diagramming and engineering documentation are the primary job. Choose Drawsy AI when architecture is one layer inside a project workspace that connects context, AI, agents, and implementation. Many teams can use both. --- # Resources > Definitions, use cases, comparisons, and help — the public pages already on this site, in one place. A discovery hub for public pages on https://app.drawsyai.com. HTML is canonical; Markdown mirrors are for agents. ## Learn - [AI Workspace](https://app.drawsyai.com/ai-workspace.md) — Learn what an AI workspace is and how visual work, sources, and AI share one place. - [AI Canvas](https://app.drawsyai.com/ai-canvas.md) — Explore what an AI canvas is and how visual work can connect with project context. - [Context Engineering](https://app.drawsyai.com/context-engineering.md) — Learn how sources, structure, scope, and freshness shape the context available to AI. - [Context-Aware AI](https://app.drawsyai.com/context-aware-ai.md) — Understand how AI uses project context instead of answering from a blank prompt alone. - [Connected Context](https://app.drawsyai.com/connected-context.md) — See where project sources come from and how they become usable context on the board. - [AI Coding Agents](https://app.drawsyai.com/ai-coding-agents.md) — Understand how coding agents plan, use tools, modify code, and verify software tasks. - [Visual Thinking](https://app.drawsyai.com/visual-thinking.md) — Learn why diagrams and spatial relationships help teams work through complex problems. ## Use Cases - [Developers](https://app.drawsyai.com/use-cases/developers.md) — An AI workspace for developers — repository context, Companion, and coding agents on one canvas. - [System Architecture](https://app.drawsyai.com/use-cases/system-architecture.md) — Design and understand systems with project context, AWS infrastructure maps, and DRAW.md. - [Product Roadmap](https://app.drawsyai.com/use-cases/product-roadmap.md) — Turn research and priorities into a visual product roadmap connected to execution. ## Compare - [Miro](https://app.drawsyai.com/compare/miro.md) — Best for: broad collaborative whiteboarding and workshops. Compare Miro and Drawsy AI → - [FigJam](https://app.drawsyai.com/compare/figjam.md) — Best for: product/design collaboration within the Figma ecosystem. Compare FigJam and Drawsy AI → - [Excalidraw](https://app.drawsyai.com/compare/excalidraw.md) — Best for: lightweight hand-drawn diagrams and sketches. Compare Excalidraw and Drawsy AI → - [Eraser](https://app.drawsyai.com/compare/eraser.md) — Best for: technical diagramming and engineering documentation. Compare Eraser and Drawsy AI → ## Guides - [From Architecture Diagram to Running Software](https://app.drawsyai.com/blog/from-architecture-diagram-to-running-software.md) — Understand → map → design → contextualize → agent → build → run → live preview. - [Why We Built DRAW.md](https://app.drawsyai.com/blog/why-we-built-draw-md.md) — Keeping architecture close to the code — repository, DRAW.md, canvas, and agents. - [Blog](https://app.drawsyai.com/blog.md) — Longer guides on AI workspaces, context engineering, and coding agents. ## Help - [Help Center](https://app.drawsyai.com/help.md) — Practical answers for canvas, accounts, connectors, and billing. ## More - [How to start](https://app.drawsyai.com/how-to.md) — Guest canvas first, sign in later when you need to save or share. - [About](https://app.drawsyai.com/about.md) — What Drawsy AI is and who it is for. - [Companion](https://app.drawsyai.com/companion.md) — Local bridge from the Drawsy AI canvas to coding tools on your machine. Canonical HTML: https://app.drawsyai.com/resources --- # Drawsy AI Help Center > Quick answers for canvas, accounts, connectors, agents, pricing, and collaboration. ## Quick links - **Start without an account:** https://drawsyai.com - **Sign in:** /login (Google) - **Pricing:** https://app.drawsyai.com/pricing - **Connectors (home):** https://app.drawsyai.com/#connectors - **Agents:** https://app.drawsyai.com/agents - **Contact:** shivam@drawsyai.com ## FAQ ### What is Drawsy AI? Drawsy AI is an AI-native visual workspace — a canvas that thinks with you. Think, build, and ship visually with AI agents that understand your context. You draw on an infinite canvas, connect tools and project context (Notion, Drive, GitHub, Slack, Gmail, Calendar, HydraDB, AWS), collaborate with your team, and ask AI questions grounded in your work. It is not merely a whiteboard and not merely a chatbot. The core flow is Draw → Connect → Understand → Create. Learn more on About and in the AI workspace definition at /ai-workspace. ### How do I start? Visit https://app.drawsyai.com and click Start building free. You land on the canvas at https://drawsyai.com in guest mode — no account required. Draw, connect context, and ask AI. When you need to save, share, collaborate, or use account features, sign in at /login. See /how-to for the full walkthrough. ### Can I use it without an account? Yes. Guest mode lets you open https://drawsyai.com and use the canvas without signing in. This is intentional — you should experience value before authentication. Sign in when you want persistence across devices, sharing, version history (Pro+), or team collaboration. Guest sessions may be ephemeral until you create an account. ### How does Drawsy authentication work? Drawsy uses Firebase Authentication. Choose Google, X, or email/password at /login; email accounts must verify their address before entering the workspace. New users get an account automatically and enter the canvas directly — there is no workspace picker. Forgot-password and verification emails are handled by Firebase. Profile settings live at /profile. ### Saving & sharing Free work stays on this device even when you sign in. Pro adds cloud sync for canvases, presentations, Kanban, comments, and live Kanban invitations. Authenticated billing and plan upgrades happen on the product app at /pricing. Public plan details are at https://app.drawsyai.com/pricing. ### Connectors Connectors bring documents, code, conversations, and data into your workspace so AI can use project context. You control what Drawsy can access — it reads only what you authorize. Basic connectors are on Free; all connectors unlock on Pro and above. See the Connectors section on the home page (/#connectors) for the full list. Connector scope does not grant Drawsy unlimited memory — context is assembled from what you connect at question time. ### Agents Agents are task-oriented AI runs scoped to canvas regions and connected sources. They help expand diagrams, synthesize research, draft decision records, and generate structure on the board. Agent runs are metered by plan: 3 per month on Free, 15 on Pro, unlimited on Team. Normal chat is for quick Q&A; agents are for heavier tasks that change canvas structure. See /agents for examples and limits. ### Pricing & plans Free ($0): local canvases, presentations, and Kanban remain on this device. Pro ($6.90/month globally; ₹499/month in India): cloud sync, comments, and live Kanban invitations. International monthly checkout is not available yet. Team and Enterprise self-serve plans are planned for later. Enterprise: custom pricing with SSO, audit logs, and dedicated support. Full matrix at https://app.drawsyai.com/pricing. No credit card required to start. ### Collaboration Real-time collaboration, team workspaces, permissions, and roles are Team-plan features. Free and Pro users can share exports and work solo or async. For collaboration questions or enterprise needs, contact shivam@drawsyai.com. For onboarding philosophy, see [How to start](https://app.drawsyai.com/how-to.md). HTML: https://app.drawsyai.com/help --- # Drawsy AI Blog > Notes from the canvas. ## Published - [The AI Workspace Is Changing](https://app.drawsyai.com/blog/ai-workspace-context-canvas-agents.md) — from chatbots to context, canvases, and agents (pillar). - [How to Use a Local AI Coding Agent with an AI Canvas](https://app.drawsyai.com/blog/local-ai-coding-agent-ai-canvas.md) — Codex CLI, OpenCode, and connecting a local coding agent to a visual AI canvas via Drawsy Companion. - [Context Engineering for AI Agents](https://app.drawsyai.com/blog/context-engineering-for-ai-agents.md) — how to give AI the right information (instructions, knowledge, memory, tools, feedback). Index: https://app.drawsyai.com/blog RSS: https://app.drawsyai.com/blog/rss.xml Authors: https://app.drawsyai.com/authors/shivam-nimbalkar · https://app.drawsyai.com/authors/drawsy-team --- # Legal & Business Information Drawsy AI is a product of **Doable Works**. Drawsy is owned and operated by Doable Works. ## Legal business name The legal name of the business that operates Drawsy AI is Doable Works (Partnership). Registered address: 4th Floor, Bldg No. 14 / Flat No. 13, Navjivan Cooperative Housing Society, Lamington Road, Dr Dadasaheb Bhadkamkar Marg, Mumbai Central, Mumbai, Maharashtra 400008, India Drawsy AI is the product brand. Doable Works is the legal entity responsible for the service at https://app.drawsyai.com and https://drawsyai.com. ## Contact Email: shivam@drawsyai.com Grievance Officer: Shivam Nimbalkar (shivam@drawsyai.com) ## Related - Privacy: https://app.drawsyai.com/privacy - Terms: https://app.drawsyai.com/terms - Refunds: https://app.drawsyai.com/refund-policy - Acceptable use: https://app.drawsyai.com/acceptable-use - HTML: https://app.drawsyai.com/legal --- # Drawsy Terms of Service Effective date: 31 October 2026. Last updated: 2 November 2026. Drawsy AI ("Drawsy") is a product of Doable Works. Drawsy is owned and operated by Doable Works, a Partnership registered in India at 4th Floor, Bldg No. 14 / Flat No. 13, Navjivan Cooperative Housing Society, Lamington Road, Dr Dadasaheb Bhadkamkar Marg, Mumbai Central, Mumbai, Maharashtra 400008, India. See https://app.drawsyai.com/legal. By creating an account, buying a paid plan, or using Drawsy (https://app.drawsyai.com, https://drawsyai.com, and /login) you agree to these Terms, the Privacy Policy, the Refund & Cancellation Policy, and the Acceptable Use Policy. ## Summary - You must be 18+ to create an account or buy a paid plan - Use the product lawfully; see https://app.drawsyai.com/acceptable-use - Guest mode and paid plans are subject to fair-use limits - You retain ownership of content you create; you own AI output from your inputs to the extent the law allows - Payments are processed by PayU; Pro renews monthly until cancelled; failed renewal returns you to Free - Governing law: India; courts at Mumbai, Maharashtra - Refunds: https://app.drawsyai.com/refund-policy - Contact shivam@drawsyai.com Full HTML: https://app.drawsyai.com/terms --- # Refund & Cancellation Policy Effective date: 31 October 2026. Last updated: 2 November 2026. Drawsy AI is a product of Doable Works. This policy covers Pro billing, cancellation, and refunds. Payments are processed by PayU. ## Summary - Digital delivery only — no physical shipping - Cancel anytime from Billing; access continues to cycle end - First Pro payment: full refund within 7 days on request - Renewals are not refunded or prorated - Always refund duplicates, post-cancellation charges, non-activation, and our billing errors - Refunds may be refused after Terms or Acceptable Use suspension - Failed debits usually reverse in 5–7 business days - Refund requests: email shivam@drawsyai.com with subject "Refund request" - Contact: shivam@drawsyai.com · 4th Floor, Bldg No. 14 / Flat No. 13, Navjivan Cooperative Housing Society, Lamington Road, Dr Dadasaheb Bhadkamkar Marg, Mumbai Central, Mumbai, Maharashtra 400008, India Full HTML: https://app.drawsyai.com/refund-policy --- # Acceptable Use Policy Effective date: 31 October 2026. Last updated: 2 November 2026. Drawsy AI is a product of Doable Works. This AUP forms part of the Terms of Service. ## Summary - Do not upload or generate unlawful, infringing, harmful, or deceptive content - AI: no malware, deepfake deception, safety bypasses, or unreviewed high-stakes advice - Share rooms and links only with people who should see the content - Connect only authorised sources; respect other people's data - No probing, scraping at scale, payment bypass, or account reselling - Report misuse or security issues to shivam@drawsyai.com Full HTML: https://app.drawsyai.com/acceptable-use --- # Drawsy Privacy Policy Effective date: 31 October 2026. Last updated: 2 November 2026. Drawsy AI ("Drawsy") is a product of Doable Works, which owns and operates it. This policy applies to the services at https://app.drawsyai.com, https://drawsyai.com, and related web, account, billing, canvas, workspace, connector, and Drawsy Companion features. Legal business name: Doable Works (Partnership) — https://app.drawsyai.com/legal Registered address: 4th Floor, Bldg No. 14 / Flat No. 13, Navjivan Cooperative Housing Society, Lamington Road, Dr Dadasaheb Bhadkamkar Marg, Mumbai Central, Mumbai, Maharashtra 400008, India Privacy contact: shivam@drawsyai.com Also applies: Terms, Refund Policy, and Acceptable Use (https://app.drawsyai.com/acceptable-use). ## Information we collect Depending on the features used, Drawsy may process: - Account and authentication data from Firebase Authentication, including provider account identifier, name, email address, profile photo, email-verification status, and authentication timestamps. Drawsy does not receive Google, X, or email account passwords. - Workspace content such as project and canvas titles, drawing elements, text, images and files, presentations, comments, Kanban boards, cards, checklists, links, timestamps, and versions. - Connected-account data such as provider, account name, email, avatar, capabilities, OAuth scopes, and encrypted access or refresh credentials. - Billing data for paid plans: plan, billing country or currency, checkout mobile number for PayU recurring mandates, and PayU payment/subscription records. Card numbers and bank credentials stay on PayU checkout. - AI and companion data such as prompts, conversation metadata, selected canvas elements, rendered captures, source images, selected project files such as DRAW.md, generated outputs, and connected-source results explicitly attached to an AI request. - Technical, usage, and support information needed to operate, secure, debug, and support the service. ## Authentication and Google Workspace data Google, X, and email/password sign-in authenticate a Drawsy account through Firebase Authentication. Email/password accounts must verify their email address before Drawsy creates an application session. This is separate from the optional Google Workspace connector. The Google Workspace connector requests identity scopes plus read-only access to Gmail messages, Calendar calendars and events, and Google Drive files and content. Drawsy uses this access only to show the connected account and to search, list, or read authorized source items for user-requested connected-context or AI features. It does not write to, delete from, or send through Gmail, Calendar, or Drive through this connector. A Google Workspace source is attached to an AI turn only when the user selects or authorizes it. Drawsy uses short-lived, capability-scoped grants and does not create a separate searchable copy of Google Workspace content by default. A copy that the user saves into a canvas, conversation, export, or project becomes content stored through that feature. The use of information received from Google Workspace APIs will adhere to the Google User Data Policy, including the Limited Use requirements. Google Workspace data is not sold, used for advertising or ad targeting, shared with data brokers, used for credit decisions, or used to train or improve generalized AI or machine-learning models. It is used only for visible, user-facing Drawsy features that the user requests or enables. Drawsy does not permit routine human access to Google Workspace content. Access is limited to providing a requested feature, investigating security or abuse, complying with law, or specific support for which the user gives affirmative consent. See the [Google API Services User Data Policy](https://developers.google.com/terms/api-services-user-data-policy) and [Google Workspace API User Data and Developer Policy](https://developers.google.com/workspace/workspace-api-user-data-developer-policy). ## Payments Payments for paid plans are processed by PayU. PayU collects payment details on its checkout under its own privacy policy and shares transaction status with Drawsy. Billing records may be retained for tax and accounting obligations (typically up to 8 years). See https://app.drawsyai.com/refund-policy. ## AI processing Drawsy AI may send the user's message and selected context to the AI runtime and model provider used for that request. Depending on setup, this may be a local Codex or OpenCode runtime, a hosted Drawsy runtime, or a provider configured with the user's own API key. Drawsy does not use workspace or Google Workspace data to train generalized AI models. Model providers may apply their own privacy policies. Drawsy Companion stores conversation indexes and preferences locally. The underlying runtime may maintain local conversation history. Session-only provider keys are removed when the session ends or the companion restarts and are not stored in the Drawsy workspace or browser storage. ## Workspace and browser storage Guest canvases and local-first data can be stored in browser local storage and IndexedDB, including drafts, files, libraries, theme preferences, and workspace state. Signed-in workspaces synchronize account-scoped metadata and encrypted scene data with Drawsy backend storage. Connector credentials are encrypted separately. Real-time collaboration may transmit canvas state, changes, cursor or presence information, and session files to collaboration infrastructure and other participants. Sharing a room or export can make content available to anyone with the relevant link or file. ## Cookies, monitoring, and service providers Drawsy uses essential browser storage and authentication state. The production product may use Simple Analytics for aggregate page views and limited product events when enabled, and Sentry for error monitoring such as URLs without fragments, release information, browser context, error messages, console errors, and stack traces. Service providers process information as needed for hosting, authentication, encrypted storage, delivery, payment processing (PayU), security, monitoring, analytics, and support. Connected providers receive data only through permissions the user approves. Drawsy does not sell personal data or transfer Google Workspace data to advertising platforms, data brokers, or unrelated third parties. We may also disclose information when required by law, court order, or a valid government request, or to protect users, the public, or Drawsy. If Doable Works is involved in a merger, acquisition, or reorganisation, information may transfer as part of it under this policy or one at least as protective. ## Retention table (summary) - Account/profile: while open, then ~30 days after deletion - Workspace content: until deleted / account closed, then ~30 days - Connectors: until disconnect or account deletion - Drawsy-held AI conversations: until you delete them or the account - Billing records: up to 8 years - Support emails: ~2 years after closed - Security/error logs: ~180 days to 1 year - Browser local storage: until cleared on device ## Your rights You can request a summary, correction, update, or deletion of personal data, and withdraw consent. Under DPDP 2023 you may nominate someone to exercise rights if you die or become unable to. Email shivam@drawsyai.com. ## Security, retention, and choices Drawsy uses HTTPS, authenticated requests, account and capability boundaries, short-lived grants, server-side connector credential handling, and encryption mechanisms for stored workspace documents, scenes, and connector tokens. No service can guarantee absolute security. We retain information while needed to provide the service, maintain an account, comply with law, resolve disputes, enforce agreements, and protect security. OAuth state records and AI or connector grants expire automatically. Disconnecting a connector requests upstream revocation and deletes the stored Drawsy connection record. Local browser copies remain until removed by the user or browser. If you delete your account, we delete or de-identify account and workspace data within 30 days, except billing records and information we must keep for law, disputes, or fraud prevention. Users can disconnect connectors, revoke Google access in provider settings, delete supported workspace content, sign out, clear browser site data, and request access, correction, or deletion by emailing shivam@drawsyai.com. Identity verification may be required. Drawsy is not directed to children. You must be 18 or older to create an account or buy a paid plan. We do not knowingly collect personal information from children. Grievance Officer: Shivam Nimbalkar, Doable Works, 4th Floor, Bldg No. 14 / Flat No. 13, Navjivan Cooperative Housing Society, Lamington Road, Dr Dadasaheb Bhadkamkar Marg, Mumbai Central, Mumbai, Maharashtra 400008, India, shivam@drawsyai.com. Complaints acknowledged within 48 hours; aim to resolve within 30 days. Privacy contact: shivam@drawsyai.com --- # About Drawsy AI > A canvas that thinks with you Drawsy is an AI workspace for visual thinking and collaboration. Teams draw on an infinite canvas, connect tools and project context, collaborate in real time, and ask AI grounded in their work. Drawsy AI is a product of Doable Works. ## How it works Draw → Connect → Understand → Create. ## Contact shivam@drawsyai.com ## Links - What you can build: https://app.drawsyai.com/#what-you-can-build - Use cases: https://app.drawsyai.com/#use-cases - Connectors: https://app.drawsyai.com/#connectors - Agents: https://app.drawsyai.com/agents - Compare: https://app.drawsyai.com/compare - How to start: https://app.drawsyai.com/how-to - Pricing: https://app.drawsyai.com/pricing - Legal: https://app.drawsyai.com/legal - Canvas: https://drawsyai.com --- # What Is an AI Workspace? > An AI workspace is a persistent environment where visual work, connected sources, and AI share the same context — not a chat window bolted onto a separate tool. Drawsy AI is an example of this approach, combining an infinite canvas, connected project context, and AI agents in one workspace. ## Definition An AI workspace is a single environment where you sketch ideas, attach documents and tools, collaborate with teammates, and ask AI questions that can see the work in front of you. Unlike a general-purpose chatbot, the workspace keeps spatial layout, links between objects, and connected sources together so context does not reset every time you open a new thread. The word workspace matters: it implies a place you return to, not a disposable conversation. Boards, notes, diagrams, and references stay on the canvas while AI responses are grounded in what is actually there — plus whatever connectors you have authorized. Drawsy AI follows this model. You draw on an infinite canvas, connect GitHub, Notion, Slack, Drive, and other sources, then ask questions or run agents scoped to that combined context. The flow is Draw → Connect → Understand → Create. ## Where Drawsy AI fits Drawsy AI is an AI-native visual workspace for developers and teams. It combines an infinite canvas with connected project context and AI agents, so the system you're designing, the sources behind it, and the AI helping you work on it can live in the same place. ## Why it matters Most teams already split thinking across whiteboards, docs, tickets, and AI chats. Each surface holds part of the truth, but none of them see the whole picture. When you paste a diagram into chat, you lose spatial relationships. When you leave the whiteboard, you lose the code and docs that justified the diagram. An AI workspace reduces that reconstruction tax. Product managers can keep a roadmap beside research notes. Engineers can map architecture next to linked repositories. Researchers can compare sources without re-uploading PDFs for every question. The benefit is not magic automation — it is fewer context handoffs, clearer shared memory, and AI answers that can reference the same artifacts your team already trusts. - Less copy-paste between tools when asking AI questions - Shared visual memory for async and cross-functional teams - Decisions stay linked to the evidence and diagrams that produced them ## How it works A practical AI workspace combines four layers: a visual surface, structured objects, connected external sources, and an AI layer that reads what you expose. The [AI canvas](/ai-canvas) provides the visual surface where diagrams, notes, and project context can live. On that surface you place shapes, text, frames, and links — the spatial map of the problem. Structured objects might include cards for decisions, milestones, or open questions. Connectors pull in live context from tools your team already uses, scoped by what you authorize. When you ask AI a question, the system assembles a context bundle: relevant canvas regions, linked notes, and snippets from connected sources. The answer should cite or reflect that bundle rather than generic training data alone. Collaboration layers let others edit the same board in real time on paid team plans. - Visual layer — diagrams, sketches, and spatial grouping - Object layer — cards, links, and annotations with stable IDs - Connector layer — GitHub, Notion, Slack, Drive, Gmail, Figma, PostgreSQL, HydraDB - AI layer — Q&A and agents grounded in the bundle above ## Examples A startup founder maps a product bet on one board: user interview quotes linked from Notion, a rough service diagram, and a short list of risks. Before a investor call they ask AI to summarize open assumptions — the answer references the board, not a blank prompt. An engineering lead connects a GitHub repo and sketches a migration plan. Agents suggest sequencing based on module boundaries visible on the canvas and files in the repo. The team edits the plan together instead of debating from memory. A researcher collects sources in frames, tags evidence strength, and uses AI to compare claims across papers already linked on the workspace. When a teammate joins later, they see the map — not a wall of chat history. ## Common approaches and problems Teams often try three substitutes: chat-only AI, traditional whiteboards, or wikis. Each works for part of the job. Chat is fast but amnesiac unless you meticulously attach files every time. Whiteboards excel at workshops but rarely stay connected to code and docs. Wikis store text but struggle with spatial reasoning and quick iteration. Another failure mode is connector sprawl without curation. If everything is connected but nothing is organized, AI context becomes noisy. Good workspaces make explicit what belongs to a project and what is out of scope. Finally, over-trusting AI summaries is risky. Grounded context helps, but models can still miss nuance or hallucinate details. Treat AI output as a draft that must be checked against sources — especially for technical or legal decisions. - Chat silos — fast answers, weak persistent structure - Whiteboard-only — great visuals, weak live tool context - Wiki-only — strong text, weak spatial and iterative thinking - Ungoverned connectors — too much noise in the context bundle ## How Drawsy AI relates Drawsy AI is built as an AI workspace from the ground up — not a whiteboard with a chat sidebar. You can open the canvas at https://drawsyai.com in guest mode without signing in, sketch immediately, and connect sources when you need deeper context. Drawsy AI connects GitHub, Notion, Slack, Google Drive, Gmail, Figma, PostgreSQL, and HydraDB to the same workspace where your diagrams live. Agents can work from that combined context instead of operating as a separate chat silo. Drawsy AI does not claim perfect memory or enterprise compliance certifications it has not published. Context is scoped to what you connect and what stays on the board; guest sessions may be ephemeral until you sign in to save and share. For questions, contact shivam@drawsyai.com. ## Related concepts An AI workspace sits at the intersection of [visual thinking](/visual-thinking), [connected context](/connected-context), and [context-aware AI](/context-aware-ai). The [AI canvas](/ai-canvas) is the visual surface inside the environment. [Context engineering](/context-engineering) designs what information AI receives; [AI coding agents](/ai-coding-agents) are one way that information is used in software work. ## Try it Open the canvas: https://drawsyai.com Contact: shivam@drawsyai.com ## FAQ ### What is an AI workspace? An AI workspace is a persistent environment where visual work, connected data sources, and AI agents share the same context — not a chat window bolted onto a separate tool. Drawsy AI is an example of this approach. ### How is an AI workspace different from a whiteboard? A whiteboard is primarily a drawing surface. An AI workspace keeps visual structure and also connects project sources — docs, code, chat, and other tools you authorize — so AI can work from ongoing context, not only what you sketched. Drawsy AI combines that model with an infinite canvas and grounded agents. ### How is an AI workspace different from a chatbot? A chatbot starts with a conversation. An AI workspace starts with ongoing work. The workspace can keep visual structure, connected sources, documents, diagrams, and other project artifacts alongside the AI interaction, so the model can work from the context you're actually using. Drawsy AI combines that workspace model with an infinite canvas and connected project sources. ### Do I need to sign in to use Drawsy AI as an AI workspace? No. You can open the canvas at https://drawsyai.com in guest mode without an account. Sign in when you need to save, share, or collaborate across devices. ### What tools can connect to an AI workspace? Drawsy AI supports connectors including GitHub, Notion, Slack, Google Drive, Gmail, Figma, PostgreSQL, and HydraDB — scoped to what you authorize. --- # What Is an AI Canvas? > An AI canvas is an infinite visual surface where diagrams, notes, and other project context can live together while AI can understand and work with what's on the board. Drawsy AI is an example of this approach, combining an infinite canvas with connected project context and AI agents. ## Definition An AI canvas is a live visual board for thinking work: system diagrams, product maps, research boards, and planning sketches. Unlike a static image or a one-off whiteboard photo, the canvas stays editable — objects can be linked, grouped, framed, and connected to external sources so AI can reason about the layout and content together. The canvas can function as the visual surface of a broader [AI workspace](/ai-workspace), where connected sources and AI workflows persist around the work. The AI part is not a separate window: questions and agents can reference selected regions, linked cards, and connector-backed documents on the same board. That surface is especially useful for [visual thinking](/visual-thinking), where spatial relationships help people explore complex ideas before committing to a single explanation. ## Where Drawsy AI fits Drawsy AI treats the canvas as more than a place to draw. It becomes a working surface for diagrams, decisions, documents, connected sources, and AI-assisted workflows. The goal is to keep the visual structure of a project connected to the information behind it. ## AI canvas vs traditional whiteboard A traditional whiteboard is excellent for workshops and sketching. An AI canvas starts from that visual foundation, then adds structured objects, connected project context, and AI that can work from the board — not only a chat sidebar bolted onto a drawing tool. Drawsy AI is not positioned merely as a whiteboard with an AI chat panel. The canvas is the surface; connected context and agents are part of how work continues on that surface. ```text Traditional whiteboard → visual work → shapes / notes → collaboration AI canvas → visual work → connected context → structured objects → AI understands the workspace → agents can work from that context ``` ## A Drawsy AI canvas workflow A practical loop on an AI canvas looks less like a one-shot prompt and more like iterative visual work: ![Drawsy AI infinite canvas with diagrams, notes, and connected project context on one board](https://app.drawsyai.com/assets/hero-canvas-preview.webp) ```text Start with an idea ↓ Draw the system ↓ Connect project context ↓ Ask AI about the canvas ↓ Expand / modify / reason ↓ Build from the result ``` - Sketch first — get structure on the board before asking AI to invent it - Connect sources when answers need code, docs, or conversations - Keep follow-up edits on the same canvas so context does not reset ## What can an AI canvas contain? The useful part of an AI canvas is not simply putting a model next to a drawing surface. It is allowing AI to work from the context represented on the canvas and the sources you have explicitly connected. Depending on the product and what you authorize, that context can include: ![System architecture sketched on a Drawsy AI canvas with services and relationships visible](https://app.drawsyai.com/assets/use-cases/system-architecture.jpg) - Diagrams and shapes - Text and notes - Frames and spatial groups - Links between objects - Project references on the board - Connected documents - Repository context - Other authorized sources ## Why it matters Spatial layout carries meaning. Proximity shows relationship; lanes show phases; clusters show ownership. When AI only sees a flat text dump, that structure disappears. An AI canvas preserves it so both humans and models can use spatial cues. Teams that plan in visuals move faster in alignment meetings because everyone sees the same map. When the map is also machine-readable, you spend less time re-explaining the diagram before every AI question. ## Examples Product discovery: sticky-note clusters for pains, arrows for workflows, a frame for the MVP scope. AI suggests gaps in the journey using the visible map. Engineering: service boxes, dashed lines for async events, a linked GitHub folder on a critical service. AI answers questions about blast radius using both sketch and repo context. Workshop output: instead of photographing a physical whiteboard, facilitators build directly on an AI canvas so remote teammates and later AI queries see the same structured board. ## How Drawsy AI relates Drawsy AI's canvas is the entry point for the workspace. Open https://drawsyai.com without logging in, sketch architecture or plans, then connect GitHub, Notion, Slack, Drive, Gmail, Figma, PostgreSQL, or HydraDB when you need richer answers. Ask questions scoped to frames and links on the board. Agents can expand diagrams, summarize decisions, and propose next steps while staying on the canvas — not in a detached chat tab. Limits matter: connector access depends on plan tier, agent runs are metered, and AI can misread complex boards if selection is unclear. Verify important outputs against your sources. ## Related concepts An AI canvas is the visual surface inside an [AI workspace](/ai-workspace). It is particularly useful for [visual thinking](/visual-thinking), where spatial layout makes relationships and gaps easier to see. [Connected context](/connected-context) lets the canvas incorporate information from authorized project tools. [Context-aware AI](/context-aware-ai) works from that project context rather than a blank prompt. [AI coding agents](/ai-coding-agents) are one way agents act on canvas and repository context together. ## Try the canvas Open Drawsy AI without an account: https://drawsyai.com ## FAQ ### What is an AI canvas? An AI canvas is an infinite visual surface where diagrams, notes, and other project context can live together while AI can understand and work with what's on the board. ### How is an AI canvas different from a whiteboard? A whiteboard focuses on drawing and collaboration. An AI canvas keeps that visual work and adds structured objects, connected project sources, and AI that can work from the board — not only a chat window beside a sketch. ### Can AI understand what's on an AI canvas? In Drawsy AI, AI can work from selected regions, frames, linked objects, and authorized connected sources — not from every pixel of an unbounded board by default. Clear selection and curated context produce better answers than dumping the entire canvas into a prompt. ### What can you use an AI canvas for? Common uses include system architecture, product planning, research maps, design exploration, and developer workflows where diagrams need to stay linked to docs, code, and conversations. ### Is Drawsy AI an AI canvas or an AI workspace? Drawsy AI is an AI-native visual workspace built around an infinite canvas. The canvas is the visual surface where project information, diagrams, and connected context come together; the workspace includes the surrounding context, tools, collaboration, and AI agents that work with it. --- # What Is Context Engineering? > Context engineering is the practice of designing what information an AI system sees — sources, structure, scope, and freshness — so answers stay useful and grounded. Drawsy AI applies these ideas to visual project work by combining canvas content, connected sources, and AI agents in one workspace. ## What is context engineering? Context engineering focuses on designing the information available to an AI system. It sits alongside prompt writing: prompts tell the model what to do; context engineering tells it what to know. The practice asks a practical question: what information should the AI receive for this task? That usually breaks down into four design choices — sources, structure, scope, and freshness — so answers stay useful and grounded instead of generic. Context engineering is not a single algorithm. It is a set of product and workflow decisions that make AI repeatable across a project, not just clever for one pasted prompt. How the system then uses that information is the territory of [context-aware AI](/context-aware-ai). ## Why does context matter? Model capability has outpaced default context delivery. A strong model with the wrong context produces confident wrong answers. A moderate model with well-curated project context often beats a premium model guessing from a one-line prompt. For teams, context engineering is how you make AI repeatable. Onboarding a new teammate means showing them the workspace, not re-explaining which Slack threads matter. For AI, the same principle applies programmatically. Security also lives here. Connecting Gmail or a database without scoping rules is dangerous. Good context engineering respects least privilege: only the project, repo, or folder needed for the task. - Grounded answers depend more on context quality than model size alone - Repeatable AI workflows require designed context pipelines - Least-privilege source scope reduces accidental data exposure ## What counts as context? Context is anything the model is allowed to use when answering or acting — not only the text inside the latest user message. - Instructions and task framing - Selected canvas regions, diagrams, and notes - Linked documents and knowledge bases - Repository files and code structure - Conversation or decision history that is still relevant - Tool outputs and connector snapshots you authorize ## Sources, structure, scope, and freshness Four dimensions show up in almost every useful context design. Miss one and the system can look smart while still being wrong. ```text CONTEXT │ ┌────────────┼────────────┐ ↓ ↓ ↓ Sources Structure Scope │ Freshness ``` - Sources — Where does the information come from? Repos, docs, canvas objects, chat, databases, or other authorized tools. - Structure — How is the information organized? Frames, links, metadata, chunking, and relationships that preserve meaning. - Scope — What should the model consider for this task? A checkout service frame and one repo — not every file in the company. - Freshness — How current is the information? Code may change hourly; strategy docs weekly. Stale snapshots need clear refresh rules. ## How do you design useful context? A practical pipeline usually has five stages: authorize sources, ingest or index content, map workspace objects to external IDs, retrieve relevant slices at query time, and assemble a bounded context package for the model. Visual workspaces add a step: serialize selected frames, linked notes, and spatial relationships into text or structured data the model can parse. Pure document retrieval misses that layout unless you engineer for it. Then evaluate. Spot-check answers against source ground truth. If the model cites the wrong PRD section or invents a dependency, fix the retrieval and scoping rules — not only the prompt wording. - Authorization — scoped tokens per source - Ingestion — chunking, metadata, and object linking - Retrieval — relevance, recency, and user selection - Assembly — token budgets and citation-friendly formatting - Evaluation — check answers against source ground truth ## Context engineering vs prompt engineering Prompt engineering designs the instruction. Context engineering designs the information available. They are not replacements for each other. A well-written instruction can still produce poor results when the model does not have the information required to complete the task. | Prompt engineering | Context engineering | | --- | --- | | Designs the instruction | Designs the information available | | Focuses on the prompt | Focuses on the broader context | | Often task-level | Often workflow/system-level | | "What should I ask?" | "What should the AI know?" | ## Context engineering vs RAG Retrieval-augmented generation (RAG) is a technique for retrieving relevant information and inserting it into the model’s input. Context engineering is the broader practice of deciding what information the AI should receive, how it is structured, what is in scope, and how current it is. RAG can be one component of a context-engineering approach. It is not the whole practice. You still need source authorization, workspace structure, selection rules, freshness, and evaluation — especially when context includes a visual board, not only document chunks. ```text RAG → a technique for retrieving relevant information Context engineering → the broader practice of deciding what information the AI should receive, how it is structured, what is in scope, and how current it is ``` ## A software project example Imagine asking an AI coding agent to modify an authentication system. With only repository context, the agent can see what exists: ```text Repository ├── auth/ ├── middleware/ ├── database/ └── tests/ ``` ### What repository context alone may miss The agent may still not know why the architecture was chosen, which constraints are intentional, which documentation is current, or which services depend on the change. That missing layer is where context engineering matters. ```text Repository + Architecture + Documentation + Decisions + Current project state ↓ Context ↓ AI coding agent ``` - Why was this architecture chosen? - What constraints exist? - Which decisions were intentional? - Which documentation is current? - What services depend on this? ## Why context matters for AI agents Agents take actions with tools. Without curated context they guess from generic training data, call the wrong APIs, or edit the wrong files. Good context engineering reduces hallucinations and wrong tool calls by narrowing what the agent is allowed to see and act on. For software development, these principles become particularly important when working with [AI coding agents](/ai-coding-agents) — systems that plan and execute tasks best when they can see repositories, architecture, and decisions, not only isolated files. ## More context is not always better Too little context means the model misses relevant information. Too much context introduces noise: similarly worded docs from different initiatives, stale notes, and unused connectors compete for attention inside a token budget. Curated context selects, structures, and scopes what matters for the task. Context boundaries matter. Connecting every source does not automatically make AI smarter — and connector sprawl often makes answers worse. - Too little context — relevant information is missing - Too much context — irrelevant information introduces noise - Curated context — relevant information is selected, structured, and scoped ## How Drawsy AI relates to context engineering Drawsy AI treats project context as part of the workspace rather than something you reconstruct inside every prompt. The canvas can hold diagrams, notes, decisions, and other project artifacts, while connected sources provide additional information that you authorize. AI agents can then work from that combined context. You choose which tools to connect; Drawsy AI does not silently ingest your entire Google Drive. The Draw → Connect → Understand → Create flow is a human-facing context-engineering loop: draw the map, connect sources, ask grounded questions, then create plans or artifacts. Start at https://drawsyai.com and add connectors as trust grows. Limits to acknowledge: retrieval is not perfect, large boards need explicit framing, and agent usage varies by plan. Treat AI output as assisted drafting. Contact shivam@drawsyai.com for connector or security questions. ```text Canvas + Connected sources + Project artifacts ↓ Shared project context ↓ AI ``` ## Related concepts Context engineering focuses on designing the information available to an AI system. [Context-aware AI](/context-aware-ai) focuses on how an AI system uses relevant context when responding or acting. [Connected context](/connected-context) describes where that project information comes from. An [AI workspace](/ai-workspace) is the environment where visual work, sources, and AI share context; an [AI canvas](/ai-canvas) is the visual surface inside it. For developer workflows, apply the same principles to [AI coding agents](/ai-coding-agents). ## Try it Open the canvas: https://drawsyai.com ## FAQ ### What is context engineering? Context engineering is the practice of designing what information an AI system sees — sources, structure, scope, and freshness — so answers stay useful and grounded. ### How is context engineering different from prompt engineering? Prompt engineering designs the instruction. Context engineering designs the information available. A strong prompt still fails when the model lacks the project information required for the task. ### How is context engineering different from RAG? RAG is a retrieval technique. Context engineering is the broader practice of deciding what information the AI should receive, how it is structured, what is in scope, and how current it is. RAG can be one part of that practice. ### Why does context matter for AI agents? Agents take actions with tools. Without curated context they guess, call the wrong tools, or edit the wrong files. Good context engineering narrows what an agent can see and act on. ### Is more context always better for AI? No. Too little context misses relevant information; too much introduces noise. Curated context selects, structures, and scopes what matters for the task. ### How does Drawsy AI use project context? Drawsy AI keeps diagrams, notes, and decisions on a canvas, connects authorized sources, and lets AI agents work from that combined project context — rather than reconstructing everything inside every prompt. --- # What Is Context-Aware AI? > Context-aware AI is AI that uses relevant information about the task, user, project, or environment when generating a response or taking an action. Instead of relying only on a blank prompt, it can use authorized context such as documents, code, conversations, or workspace state. Drawsy AI applies this approach to visual project work by giving AI access to the canvas and connected project sources you choose to expose. ## What is context-aware AI? Context-aware AI conditions its answers and actions on information beyond the latest user message. That can include what is on a workspace, what you authorize from connected tools, and what is in scope for the current task. Awareness is bounded. Models have token limits; connectors have permissions; boards can be large. Useful systems retrieve and rank what matters rather than pretending to know everything. Context-aware AI focuses on how an AI system uses relevant context when responding or acting. [Context engineering](/context-engineering) focuses on designing the information available to that system. ## Context-aware AI vs ordinary AI Without project context, the model mainly sees your prompt and whatever conversation history is present. With relevant context, it can also see authorized project information — and still may be wrong. Context improves grounding; it does not guarantee correctness. ```text Without project context User ↓ Prompt ↓ AI ↓ Generic response With relevant context User ↓ Prompt + Project context ↓ AI ↓ Context-aware response ``` ## What counts as context? Different systems expose different layers. A useful taxonomy looks like this: - User context — preferences, goals, and standing instructions - Task context — what you are trying to accomplish right now - Project context — architecture, requirements, decisions, and documents - Tool context — information retrieved from connected systems you authorize - Workspace context — what is currently represented on the canvas or board ## Context-aware AI vs RAG Context-aware AI is a behavior or system characteristic: the model can appropriately use relevant external or environmental information. Retrieval-augmented generation (RAG) is one technique that can help supply that information. They are not the same thing. A system can retrieve documents and still ignore them; another can be context-aware using selection, tools, and workspace state without a classic vector-search pipeline. For how to design sources, structure, scope, and freshness, see [context engineering](/context-engineering). ## Context-aware AI vs chatbots A chatbot interface answers through conversation. That alone does not decide whether the system is context-aware. A chatbot can also be context-aware — the distinction is not the chat UI itself; it is whether the system can appropriately use relevant external or environmental context. A typical chat session may rely on the conversation and whatever you paste in. A context-aware system can additionally incorporate project state, documents, code, workspace content, and connected tools — when those sources are authorized and in scope. ```text Typical chat context → conversation → whatever you provide in the thread Context-aware system (may also include) → project state → documents → code → workspace → connected tools ``` ## Example: planning a new service Ask a generic model: “Design an API for a notification service.” It can produce a plausible architecture from public patterns — naming conventions, queue choices, and endpoint shapes that may not match your stack. With project context — existing architecture, current API conventions, database constraints, product requirements, and open decisions — the same request can stay inside those boundaries: reuse patterns your team already uses, surface conflicts with known constraints, and call out decisions that are still unresolved. The difference is behavior under constraints, not a longer marketing claim about “smarter AI.” ```text Without context → generic notification-service design With project context → existing architecture → current API conventions → database constraints → product requirements → open decisions ↓ Response shaped by those constraints ``` ## How context-aware systems work At query time the system typically builds a context package: selected workspace elements, metadata from linked objects, and retrieved passages from connectors. Prompt policies tell the model to prefer that package, cite when possible, and admit uncertainty when context is missing. Some features add tool use — fetching a fresh file from a repo or re-querying a data source — but the principle stays the same: ground before generate. User actions shape awareness. Framing a diagram, linking a doc, or tagging a decision card tells the system what belongs in scope. - Selection and framing on visual workspaces - Connector retrieval with permission checks - Prompt policies that prioritize grounded snippets - Explicit uncertainty when context is incomplete ## Context-aware does not mean context-perfect Connected sources can be incomplete. Information can be stale. Permissions and scope matter. Models can misunderstand or over-weight the wrong snippet. Humans should verify consequential outputs — especially for technical, legal, or customer-facing decisions. When AI cites your docs, errors in those docs propagate. Treat context-aware AI as an interface to project information, not an oracle. - Incomplete or stale sources still produce confident answers - Over-broad scope adds noise and weakens grounding - Verification remains required for high-stakes work ## How Drawsy AI uses context Drawsy AI combines the visual context represented on the canvas with authorized connected sources, allowing AI and agents to work from the project information you choose to expose — rather than only a blank chat prompt. You control connectors and what stays on the board. Try a narrow workflow at https://drawsyai.com: sketch a small system, link one doc or repo, frame the diagram, ask a specific question, and check the answer against your source. Drawsy AI does not claim perfect memory or perfect understanding. Context is scoped, revocable, and subject to model limits. Agent and connector capacity vary by plan. ```text Drawsy AI │ ┌─────────┴─────────┐ ↓ ↓ Canvas Connected sources │ │ └─────────┬─────────┘ ↓ Project context ↓ AI / agents ``` ## Related concepts Context-aware AI focuses on how an AI system uses relevant context when responding or acting. [Context engineering](/context-engineering) focuses on designing the information available to an AI system. [Connected context](/connected-context) describes the project information brought in from authorized sources. An [AI workspace](/ai-workspace) is where visual work, sources, and AI share context; an [AI canvas](/ai-canvas) is the surface that holds workspace state. [AI coding agents](/ai-coding-agents) are one place this behavior shows up in software tasks. ## Try it Open the canvas: https://drawsyai.com ## FAQ ### What is context-aware AI? Context-aware AI is AI that uses relevant information about the task, user, project, or environment when generating a response or taking an action — not only a blank prompt. ### How is context-aware AI different from a chatbot? A chatbot is an interface. Context-aware AI is about whether the system can appropriately use relevant external or environmental context. A chatbot can also be context-aware. ### What information can context-aware AI use? Depending on the system and what you authorize: user and task context, project documents and decisions, code, workspace state, and information from connected tools. ### Is context-aware AI the same as RAG? No. Context-aware AI describes behavior. RAG is one technique for retrieving information to supply that behavior. See context engineering for how to design the broader information pipeline. ### Does context-aware AI always give better answers? Not always. Better grounding helps when context is relevant and current, but incomplete sources, noise, stale data, and model mistakes still happen. Verify important outputs. ### How does Drawsy AI use context-aware AI? Drawsy AI combines canvas content with authorized connected sources so AI and agents can work from the project information you choose to expose — rather than only a blank prompt. --- # What Are AI Coding Agents? > AI coding agents are systems that plan and execute software tasks with tool access — they work best when they can see repositories, architecture, and decisions, not just isolated files. Drawsy AI connects that agent workflow to visual project context on an infinite canvas. ## What are AI coding agents? AI coding agents go beyond suggesting the next line of code. They take a software goal, break it into steps, use tools to inspect or change a codebase, and often verify work with tests, linters, or diffs before reporting back. Capabilities vary by product and configuration. Some agents stay close to the editor; others run in a CLI, cloud environment, or workspace with broader tool access. Autonomy is not unlimited — most useful setups still include human review, scoped permissions, and clear task boundaries. ```text AI coding agent ↓ understands a task ↓ plans ↓ uses tools ↓ reads / changes code ↓ runs / verifies work ``` ## Not autocomplete, not a single chat reply Autocomplete predicts tokens in the current file. Traditional chat answers a question and stops until you prompt again. Simple code generation produces a snippet from a description. A coding agent is built for longer task loops: search the repo, edit multiple files, run commands, check results, and continue toward the goal — within the tools and policies you give it. - Autocomplete — next token or line in place - Chat — answer a prompt, usually without a multi-step tool loop - Code generation — produce a snippet from a description - Coding agent — plan, use tools, iterate, and verify toward a goal ## Coding agent vs coding assistant People often use “assistant” and “agent” interchangeably. The useful distinction is the interaction loop: an assistant typically responds to a prompt; an agent can take multiple steps and call tools. Exact behavior depends on the particular agent or tool configuration — do not assume every “agent” product can rewrite your monorepo unattended. | Coding assistant | Coding agent | | --- | --- | | Suggests code | Can plan a task | | Usually responds to a prompt | Can take multiple steps | | Developer directs each action | Agent can use tools | | Smaller interaction loop | Longer task execution loop | ## How do AI coding agents work? A typical loop looks like: receive a goal → gather relevant files and docs → propose a plan → use tools (read, write, search, terminal) → verify → report. Some loops pause for human approval before writes or risky commands. Context usually comes from the repository, explicit file selection, issue trackers, and — increasingly — architecture maps and linked specs. Retrieval and selection matter: dumping an entire monorepo into every step is expensive and often noisier than a curated slice. - Goal decomposition and tool calls - Retrieval over repositories and linked docs - Human-in-the-loop approval for risky operations - Verification via tests, linters, or diff review ## The context problem A coding agent may have access to a repository and still lack important project context. Files show what exists; they do not always show why boundaries were drawn, which constraints are intentional, or which docs are still authoritative. That gap is where [context engineering](/context-engineering) applies: designing sources, structure, scope, and freshness so the agent receives useful information for the task — not every available source by default. ```text Repository + Architecture + Documentation + Decisions + Current project state ↓ Better task context ↓ Coding agent ``` ## What context does a coding agent need? The right context depends on the task. Giving an agent every available source can create noise instead of improving its decision-making. - Code — relevant files, modules, and APIs - Architecture — service boundaries and data flows - Requirements — what success looks like for this change - Documentation — specs that are still current - Decisions — ADRs and intentional trade-offs - Dependencies — what else breaks if this changes - Tests — how correctness is checked today - Project constraints — security, performance, or compliance limits ## A concrete software example An engineer asks an agent to change authentication middleware. The repository can reveal the current implementation: handlers, session logic, tests, and call sites. The agent may still need architecture constraints (where identity is validated), service dependencies (what breaks if tokens change), previous design decisions, relevant documentation, and current project requirements. With only local files, it can produce a plausible patch that violates an intentional boundary. Better task context changes the loop: the agent plans against known constraints, edits with clearer scope, and humans review against the same map of services and decisions — not only a raw diff. ## Why it matters Software work is more than typing. It is navigation, trade-offs, and alignment with existing patterns. Agents that only see the current file recreate wheels or violate conventions because they miss the map. Autonomy also raises stakes. Unguarded agents can delete tests, leak secrets into logs, or ship changes nobody reviewed. Teams need boundaries: branch policies, sandbox environments, scoped tokens, and clear tasks. - Code tasks need repository and architectural context - Shared project memory improves agent plans and human review - Autonomy demands guardrails — review, sandboxes, scoped access ## How Drawsy AI relates Drawsy AI gives coding workflows a visual project context around the agent. Developers can map architecture, connect project sources, and use Drawsy Companion to bridge the canvas with tools such as Codex CLI and OpenCode CLI. Sketch boundaries on the board at https://drawsyai.com, connect a repo or docs when you need richer answers, and keep planning next to the same context the agent should respect. Drawsy AI Agents help with workspace-grounded research, structure, and analysis — they are not a replacement for your editor agent or CI. Companion is a local bridge: your project files stay on your machine; whether prompts reach an external model still depends on the coding agent and provider you configure. Agent runs and connectors vary by plan. Always review diffs and run your normal verification. ```text Canvas ↓ Architecture + Project sources + Repository ↓ Drawsy Companion ↓ Coding agent ``` ## Related concepts Coding agents need useful project context — from [connected context](/connected-context), shaped by [context engineering](/context-engineering), and used as [context-aware AI](/context-aware-ai). Developers can apply these workflows in an [AI workspace for developers](/use-cases/developers). An [AI workspace](/ai-workspace) is where visual work, sources, and AI share context; an [AI canvas](/ai-canvas) is the surface for architecture and decisions. For the local bridge to Codex CLI and OpenCode CLI, see [Drawsy Companion](/companion). ## Try it Open the canvas: https://drawsyai.com Companion: https://app.drawsyai.com/companion ## FAQ ### What are AI coding agents? AI coding agents are systems that plan and execute software tasks with tool access — they work best when they can see repositories, architecture, and decisions, not just isolated files. ### How are AI coding agents different from coding assistants? Assistants typically respond to a prompt and suggest code. Agents can plan multi-step work, call tools, and iterate toward a goal. Exact autonomy depends on the product and how it is configured. ### How do AI coding agents work? A common loop is: take a goal, gather relevant context, plan steps, use tools to read or change code, verify results, and report. Many setups pause for human approval before risky writes. ### What context does an AI coding agent need? It depends on the task — usually a mix of relevant code, architecture, requirements, docs, decisions, dependencies, tests, and constraints. More sources are not always better; curated scope reduces noise. ### Can AI coding agents understand an entire codebase? Not perfectly, and not all at once. Agents work from retrieved or selected slices of a repository plus any other context you provide. Large codebases still need scoping, structure, and human review. ### How does Drawsy AI work with AI coding agents? Drawsy AI provides a visual canvas for architecture and decisions, connected project sources, and Drawsy Companion to bridge the canvas with local agents such as Codex CLI and OpenCode CLI — so coding work stays tied to project context. --- # What Is Visual Thinking? > Visual thinking is a way of exploring ideas and problems through diagrams, maps, spatial relationships, and other visual representations. By making relationships and structure visible, visual thinking can help people explore complexity before committing to a particular explanation or solution. Drawsy AI provides an infinite canvas for this kind of work, with AI and connected project context available alongside the visual representation. ## What is visual thinking? Visual thinking is the practice of externalizing cognition on a surface: whiteboards, paper, sticky notes, or digital canvases. You draw boxes for concepts, arrows for flows, clusters for themes, and lanes for time or ownership. The surface becomes a shared thinking aid, not just decoration. It complements verbal and textual reasoning. Some problems — system boundaries, user journeys, option trade-offs — are easier to see than to narrate. Visual thinking makes those structures explicit so teams can critique them. In software and product work, visual thinking spans napkin sketches, architecture maps, story maps, and research synthesis boards. The medium changes; the goal is the same: make the invisible structure visible. ## Why visual thinking works Visual thinking is useful when structure and relationships matter more than a single linear explanation. A few practical reasons show up again and again: - Relationships become visible — a diagram can show how components connect spatially, not only in a list - Problems become externalized — instead of keeping the entire structure in your head, you put it onto a surface - Iteration becomes cheaper — you can rearrange, group, remove, and reconnect ideas before committing to implementation - Gaps become easier to spot — missing dependencies or unexplained relationships show up as empty space or dangling arrows ## Visual thinking vs writing Visual thinking is not better than writing in every situation. The two work together. A diagram can expose the structure of an idea; writing can explain the details, constraints, and nuance. | Writing | Visual thinking | | --- | --- | | Strong for detailed explanation | Strong for relationships and structure | | Sequential | Spatial | | Precise prose | Fast exploration | | Good for final documentation | Good for early exploration | ## What can you visualize? Visual thinking shows up across many kinds of work. The surface changes; the pattern — parts, relationships, and iteration — stays similar. - Systems — architecture, services, dependencies - Knowledge — concept maps, research relationships, evidence - Planning — roadmaps, milestones, workflows - Product design — flows, user journeys, interfaces - Research — sources, claims, and relationships - Presentations — ideas organized spatially before a final narrative ## A visual thinking workflow A practical loop looks less like a finished diagram and more like continuous exploration: ![Visual thinking on a Drawsy AI canvas — systems and relationships mapped spatially](https://app.drawsyai.com/assets/use-systems.png) ```text Question ↓ Put the problem on the canvas ↓ Break it into parts ↓ Connect relationships ↓ Group / rearrange ↓ Find gaps ↓ Explain / decide / build ``` - Diverge first — explore breadth before forcing neatness - Converge when a decision matters — frame scope, owners, and next steps - Keep the map editable — visual thinking fails when the board becomes untouchable wallpaper ## Visual thinking and AI AI can participate in visual thinking by helping expand, summarize, reorganize, question, or transform material represented on the canvas. The usefulness depends on the context available to the system and the quality of the underlying information. That is why an [AI canvas](/ai-canvas) matters as a surface, and why [context-aware AI](/context-aware-ai) matters as a behavior: the model needs access to what you actually drew and authorized — not only a blank prompt about a diagram you cannot see. ```text Human visual thinking ↓ Canvas ↓ Context ↓ AI ↓ Expand / question / synthesize ``` ## When visual thinking isn't the right tool Visual thinking is most useful when relationships, structure, uncertainty, or iteration are important — not as a replacement for every other form of communication. - A detailed API specification still needs written documentation - A legal agreement needs precise text - A database schema may eventually need a formal schema definition - A simple task may not need a diagram at all ## Why it matters for teams Alignment meetings fail when participants hold different mental models. A shared diagram forces concrete questions: Is this arrow synchronous? Does this box own data? Which path is in scope? Visual artifacts also age better than meeting memory. For remote and async teams, a live map reduces the need to re-explain the same structure every week — especially when new contributors can start from the board instead of a wall of chat history. ## How Drawsy AI relates Drawsy AI gives visual thinking an AI-native workspace. You can map systems, research, plans, designs, or other complex ideas on an infinite canvas, connect relevant project sources, and use AI agents to work with the context represented there. For persistent visual work, an [AI workspace](/ai-workspace) adds connected sources and AI workflows around the [AI canvas](/ai-canvas). Start visually at https://drawsyai.com without an account wall. When a diagram stabilizes, sign in to save, share, and collaborate per your plan. Drawsy AI is not claiming visual thinking replaces written specs or code — it keeps visuals, context, and AI in one loop: Draw → Connect → Understand → Create. ## Related concepts An [AI canvas](/ai-canvas) is a digital surface for this kind of visual work. For persistent visual work, an [AI workspace](/ai-workspace) adds connected sources and AI workflows around the canvas. See also [system architecture](/use-cases/system-architecture), [product roadmaps](/use-cases/product-roadmap), and [AI workspace for developers](/use-cases/developers). ## Try the canvas Open Drawsy AI without an account: https://drawsyai.com ## FAQ ### What is visual thinking? Visual thinking is a way of exploring ideas and problems through diagrams, maps, spatial relationships, and other visual representations — making structure visible before committing to a particular explanation or solution. ### Why is visual thinking useful? It externalizes structure, makes relationships visible, lowers the cost of iteration, and helps teams spot gaps that are easy to miss in linear text alone. ### What is the difference between visual thinking and mind mapping? Mind mapping is one technique — often radial, centered on a single topic. Visual thinking is broader: architecture maps, journey flows, research boards, roadmaps, and other spatial representations of relationships. ### Is visual thinking only useful for designers? No. Engineers map systems, product managers map plans, researchers map evidence, and teams of all kinds use diagrams to align on structure. ### What can you use visual thinking for? Systems, knowledge maps, planning, product design, research synthesis, and organizing ideas before presentations — anywhere relationships and structure matter. ### How can AI help with visual thinking? AI can help expand, summarize, reorganize, question, or transform material on a canvas when it has access to relevant context. Usefulness depends on that context and human review of important outputs. ### Is visual thinking better than writing? Not universally. Visual thinking is strong for structure and exploration; writing is strong for precise detail and final documentation. They work best together. --- # What Is Connected Context? > Connected context is project information from the tools and sources you already use — such as documents, code, conversations, files, and data — linked to a shared workspace so people and AI can work from the same sources. Drawsy AI brings authorized sources into the same visual workspace as your canvas, allowing AI and agents to work from project context instead of relying only on information pasted into a prompt. ## What is connected context? Connected context means your workspace can reach outward to tools where work already lives, under permissions you control, and make relevant project information available beside diagrams, decisions, and plans. It is the Connect step in Draw → Connect → Understand → Create. Connection is not bulk duplication. Useful systems link or fetch on demand, respect authorization scopes, and keep relationships explicit — for example a repository tied to a service on an architecture map, or a PRD linked to a milestone frame. This page is about the concept: where project context comes from and how sources become usable in a shared workflow. For the product list of integrations, see [Connectors](/#connectors). ## Connected, imported, pasted, or unconnected Not every way of getting information into an AI workflow is the same. The differences explain why connected context exists. - Connected — the source is linked and available within the project workflow, under authorization you control - Imported — a copy of information was brought somewhere else; the copy can drift from the system of record - Pasted — someone manually supplied a small piece of information for one prompt - Unconnected — the AI has no access to the source unless you recreate it by hand ## What kinds of sources become connected context? Project information usually lives across several systems. Grouping sources by role is clearer than a logo wall: - Code — GitHub repositories and related project code - Documents — Notion pages, Google Drive files, and similar specs - Communication — Slack threads, Gmail, and other conversation sources - Design — Figma files and related product visuals - Data — PostgreSQL, HydraDB, and other structured project data ### See the current connector list Drawsy AI documents the live connector set on the site — including GitHub, Notion, Slack, Google Drive, Gmail, Figma, PostgreSQL, HydraDB, and others. Availability can vary by plan. Browse the current list at https://app.drawsyai.com/#connectors. ## How connected context becomes usable A practical flow looks like this: choose a source, connect it, authorize access, select what belongs to the project, bring that into the workspace, then let humans and AI work from it. Connecting a source does not mean the AI should blindly use everything in it. Connected sources are only useful when their information is relevant and appropriately scoped. See [context engineering](/context-engineering) for how context can be designed. Once supplied, that project information can enable [context-aware AI](/context-aware-ai) workflows. ![Drawsy AI workspace connecting project tools and sources around a shared canvas](https://app.drawsyai.com/assets/hero-connect-scene.png) ```text Source ↓ Connect ↓ Authorize ↓ Select relevant project context ↓ Workspace ↓ AI / Agents ``` ## Connected context vs more data Connected context is not about giving AI access to everything. More sources do not automatically mean better context. Good connected context still needs relevance, permissions, scope, organization, and freshness. Without those, connector sprawl adds noise, cost, and risk — which is why [context engineering](/context-engineering) matters after sources are linked. ```text More sources ≠ Better context Useful connected context also needs → relevance → permissions → scope → organization → freshness ``` ## Humans and AI share the same sources Without connected context, people and models often work from different piles of information. Humans read Notion, Slack, and GitHub, then draw a diagram; AI receives a pasted summary that omits structure and decisions. With connected context, the workspace becomes a shared project layer: the same authorized sources can inform human planning and AI questions or agents — instead of reconstructing context inside every prompt. ```text Without connected context Human → reads Notion / Slack / GitHub → draws diagram AI → gets a pasted summary With connected context Shared project context / \ ↓ ↓ Human AI \ / ↓ ↓ Workspace ``` ## Example: planning a database migration Ask only: “How should we migrate this database?” Without connected context, the model answers from the prompt and generic patterns. With connected context, the same question can draw on schema and code in GitHub, requirements in Notion, team decisions in Slack, product flows in Figma, and the migration plan on the canvas — so analysis stays tied to the project’s actual artifacts. ```text GitHub → schema / code Notion → requirements Slack → team decisions Figma → relevant product flows Canvas → architecture + migration plan ↓ Project context ↓ AI ↓ Migration analysis ``` ## Permissions and boundaries Connected context should respect what you have authorized. A source being connected does not mean every piece of information should automatically become relevant to every task. Useful boundaries include authorization, project scope, source relevance, freshness, and human verification of consequential answers. Disconnect sources when they no longer belong to the project. Do not assume connectors imply perfect sync. Retrieval can miss files, mis-parse formats, or truncate large documents. Spot-check critical facts against the system of record. - Authorization — only what you grant - Project scope — not every account and folder by default - Relevance — select what belongs to the task - Freshness — know when snapshots may be stale - Verification — check important outputs against sources ## How Drawsy AI relates Drawsy AI connects project sources to the same canvas where teams think and plan. GitHub repositories, documents, conversations, files, and other authorized sources can become part of the project’s working context, while the canvas provides a visual layer for architecture, decisions, plans, and other work. Start narrow at https://drawsyai.com — one repo or doc — then add sources as trust grows. Browse the live connector list at https://app.drawsyai.com/#connectors. Connector availability varies by plan; see Pricing for details. Drawsy AI does not claim certifications it has not published. You control what you connect and can disconnect sources. Guest sessions may be ephemeral until you sign in. ```text Canvas + Connected sources ↓ Project context ↓ Q&A / AI agents ``` ## Related concepts Connected context describes where project information comes from. Connected sources are only useful when their information is relevant and appropriately scoped — see [context engineering](/context-engineering). Once supplied, that project information can enable [context-aware AI](/context-aware-ai) workflows. An [AI workspace](/ai-workspace) is where visual work, sources, and AI share context; an [AI canvas](/ai-canvas) holds the visual layer. [AI coding agents](/ai-coding-agents) use connected project context for software tasks. ## Try it Open the canvas: https://drawsyai.com Connectors: https://app.drawsyai.com/#connectors ## FAQ ### What is connected context? Connected context is project information from the tools and sources you already use — documents, code, conversations, files, and data — linked to a shared workspace so people and AI can work from the same sources. ### Why is connected context useful for AI? Without linked sources, AI often answers from the prompt and generic training knowledge. Connected context grounds responses in your project’s actual artifacts. ### What sources can be connected? In Drawsy AI, documented sources include GitHub, Notion, Slack, Google Drive, Gmail, Figma, PostgreSQL, HydraDB, and others listed on the Connectors section of the site. Availability can vary by plan. ### Is connected context the same as RAG? Not necessarily. Connected context describes how project information is linked and made available within a workflow. RAG is a retrieval technique that may be used to supply information to an AI system. ### Does connecting more sources make AI better? Not automatically. More sources without relevance, permissions, scope, organization, and freshness often create noise. Context engineering is how you design those boundaries. ### Can AI use every connected source? No. Authorization, project scope, and task relevance still apply. A connected source is available to the workflow — it should not silently become unbounded input for every question. ### How does Drawsy AI use connected context? Drawsy AI links authorized sources to the same canvas where teams plan and draw, so Q&A and agents can work from shared project context rather than only pasted prompts. --- # From Architecture Diagram to Running Software > Explore how architecture, project context, AI coding agents, and a visual workspace can connect the path from system design to working software. ## The gap between the diagram and the IDE Most architecture work still ends the same way: ```text Architecture diagram ↓ Developer opens IDE ↓ Architecture lives somewhere else ↓ Code lives somewhere else ↓ AI agent sees mostly the repository ↓ Context gets reconstructed manually ``` ### We wanted to shorten that gap The diagram was rarely useless. It was just disconnected. By the time someone asked a coding agent to implement a change, the architectural intent had to be rebuilt from memory, chats, and whatever files happened to be open. We wanted a workflow where the map of the system could stay visible while the software got built — then come back onto the same board as a running preview so you can compare design with reality. That is the Drawsy AI story below. The repository-side bridge for keeping architecture next to code is [Why We Built DRAW.md](/blog/why-we-built-draw-md). This article is what happens when you connect the full loop. ```text UNDERSTAND ↓ MAP ↓ DESIGN ↓ CONTEXTUALIZE ↓ AGENT ↓ BUILD ↓ RUN ↓ LIVE PREVIEW ``` ## 1. Understand the system Start with what already exists — not with a blank prompt to generate an app. On an [AI workspace for developers](/use-cases/developers), that usually means bringing the project into view before anyone draws a new box: ```text Repository + Documentation + Existing architecture + Project decisions ``` - What services already exist? - Where are the hard boundaries? - Which decisions are already locked in? - What context is missing before a change is safe? ### Understanding first — generating later The goal in this step is not immediately to generate code. It is to understand the system well enough that later agent work has something real to stand on. That selection problem — what information should be in scope, and what should stay out — is [context engineering](/context-engineering) in practice. ## 2. Map the system on the canvas Once you have sources, put the system where humans and agents can both see it: on an [AI canvas](/ai-canvas). Map services, dependencies, data flows, boundaries, components, and open questions. Leave the unknowns visible instead of pretending the diagram is finished. ![Drawsy AI canvas mapping idea, context, AI agent, plan, and build on an editable board](https://app.drawsyai.com/assets/auth-canvas.png) - Services and components as editable nodes - Dependencies and data flows you can rearrange - Boundaries and open questions kept on the same board - The diagram isn’t a screenshot — it’s an editable representation of the project ## 3. Design what should change Current-state maps are useful. Change still needs a proposed state. ```text Current system ↓ Identify constraint ↓ Explore alternatives ↓ Proposed architecture ``` ### AI explores. Humans decide. AI can help explore options, surface missing edges, and structure incomplete designs. The developer or team remains responsible for the architectural decision. That standard matters. On Drawsy AI, architecture work is assisted — not abdicated. The fuller product surface for this job is [AI System Architecture Tool](/use-cases/system-architecture). ## 4. Give the agent the context behind the design A diagram alone is still thin context. The useful unit is project context: the canvas plus the repository, requirements, architecture decisions, and relevant documents you authorize. ![Connected project sources feeding into AI context on Drawsy AI](https://app.drawsyai.com/assets/give-context.png) ```text Canvas + Repository + Requirements + Architecture + Relevant documents + Decisions ↓ Project context ``` ### Frame the task inside the architecture Instead of asking an agent to implement “add notifications,” the task can be framed inside the architecture and project context that explain where that functionality belongs — which service owns it, what it depends on, and what must not change. That is the bridge to [context engineering](/context-engineering) and [connected context](/connected-context): selected sources become usable workspace context, not a dump of everything the model can reach. Drawsy AI can provide selected project context to supported AI workflows. It does not claim to understand your entire codebase automatically. ## 5. Let the coding agent work from that context With architecture and project context on the board, a coding agent can plan against something richer than a one-line prompt. In practice that often means a local development workflow — for example Codex CLI or OpenCode — with [Drawsy Companion](/companion) bridging the Drawsy AI canvas to the agent on your machine. This article is the workflow, not a Companion landing page. ```text Architecture + Context ↓ Coding agent ↓ Plan ↓ Read ↓ Change ↓ Verify ``` ### What the agent is doing A capable agent may inspect structure, read relevant files, propose a plan, modify code, and verify with tools available in its environment. For the category definition, see [What are AI coding agents?](/ai-coding-agents). For the local canvas + agent setup, see [How to Use a Local AI Coding Agent with an AI Canvas](/blog/local-ai-coding-agent-ai-canvas). ## 6. Turn the architecture into implementation This is where the workflow diverges from a normal architecture write-up. ```text Architecture decision ↓ Agent task ↓ Code changes ↓ Tests / verification ``` ### Incremental implementation — not diagram-to-app magic The goal isn’t “generate an entire application from a diagram.” Architecture becomes one of the artifacts that informs incremental implementation. Architecture and project context can inform coding-agent workflows that produce and iterate on code — they do not automatically turn diagrams into production software. That distinction is the difference between a credible developer tool and a brochure claim. ## 7. Run the software Implementation without a running surface still leaves a gap. The Drawsy AI loop continues: ![Drawsy AI canvas with architecture diagram and live app output side by side](https://app.drawsyai.com/assets/hero-canvas-preview.webp) ```text Code ↓ Run project ↓ Expose running application ↓ Bring preview into canvas ``` ### Preview as a canvas element A running project can be surfaced as an interactive canvas element — so the preview sits beside the architecture that was supposed to describe it, not in a separate browser tab you forget to reconcile. ## 8. Compare the architecture with what you built This is the payoff. The canvas becomes a feedback surface: ```text Architecture ↕ Implementation ↕ Live software ``` - Does the implementation match the design? - What changed while we built? - What dependency is missing? - Where did the architecture evolve? - What should be documented back into the project — including DRAW.md? ### Design meeting reality When architecture, implementation, and live software share one board, you stop treating the diagram as a kickoff artifact. It becomes part of an ongoing visual development workflow — closer to architecture-driven development without pretending the diagram is the product. ## What we learned building this workflow A few lessons from building toward this loop: - **Architecture is context, not decoration.** If the map never reaches the agent or the preview, it was mostly theater. - **More context isn’t automatically better.** Irrelevant or stale sources make agent work worse. Selection matters — see [context engineering](/context-engineering). - **AI needs boundaries.** Scope the workspace, the files, and the decision the human still owns. - **The diagram shouldn’t be disconnected from implementation.** That is why DRAW.md and the canvas exist as a bridge, not a gallery. - **Agents still need verification.** Plan → change → test remains the loop; the canvas doesn’t remove it. - **The human remains responsible for architectural decisions.** AI can explore; teams decide. ## What this workflow is not claiming A few lines we refuse to blur: - Drawsy AI does not “understand your entire codebase” by default — it works from selected project context you bring into the workspace. - AI does not automatically turn diagrams into production software. - Architecture still complements Git, docs, and your IDE — it does not replace them. ## Where to go next If you want the architecture product surface: [AI System Architecture Tool](/use-cases/system-architecture). If you want the developer workspace story: [AI Workspace for Developers](/use-cases/developers). If you want the agent category: [AI Coding Agents](/ai-coding-agents). If you want the repository bridge: [Why We Built DRAW.md](/blog/why-we-built-draw-md). If you want to try the loop: open the canvas and start from a real project, not a blank pitch deck. > **TL;DR:** An architecture diagram shouldn't have to be the final artifact. On Drawsy AI it can become part of the context an AI coding agent uses to build and iterate — understand, map, design, contextualize, then agent → build → run → live preview — without claiming diagrams auto-generate production apps. ## Frequently asked questions ### Can an architecture diagram become working software? Not by magic. On Drawsy AI, architecture and project context can inform coding-agent workflows that produce and iterate on implementation — then a live preview can sit back on the canvas so you compare design with reality. ### Does Drawsy AI generate entire apps from diagrams? No. The credible path is incremental: understand, map, design, contextualize, then let agents implement changes with verification. Architecture informs the work; it does not replace engineering judgment. ### How do AI coding agents fit this workflow? Agents plan, read, change, and verify against selected project context — including architecture on the canvas. Local agents such as Codex CLI or OpenCode can connect through Drawsy Companion. ### What is live preview on the canvas? After you run the project, a running application can be brought onto the Drawsy AI canvas as an interactive element so architecture and live software sit side by side. ### How is this different from the DRAW.md article? DRAW.md explains the repository ↔ canvas bridge for keeping architecture close to the code. This article walks the full Understand → Live preview workflow those pieces enable. ## Related reading - [/use-cases/system-architecture](/use-cases/system-architecture) - [/use-cases/developers](/use-cases/developers) - [/ai-coding-agents](/ai-coding-agents) - [/context-engineering](/context-engineering) - [/connected-context](/connected-context) - [/ai-canvas](/ai-canvas) - [/blog/why-we-built-draw-md](/blog/why-we-built-draw-md) - Start on the canvas: https://drawsyai.com --- # Why We Built DRAW.md: Keeping Architecture Close to the Code > How Drawsy AI uses DRAW.md to keep architecture next to the repository — a canonical project document that can render into an editable canvas and stay available to AI coding agents. ## Architecture that lives next to the code — not in a slide deck Most teams invent the same painful pattern eventually: Someone draws a careful system diagram. It gets exported as a PNG. It lands in a slide, a wiki page, or a shared drive. Months later the code has changed — and the diagram has not. The diagram was rarely wrong on day one. It became wrong because it stopped living where the system lives. That is the problem DRAW.md is meant to address inside Drawsy AI: keep architecture close enough to the repository that visual work, project context, and [AI coding agents](/ai-coding-agents) can share the same map. Drawsy AI is an AI-native visual workspace for developers and teams. DRAW.md is how that workspace stays connected to the code, not floating beside it. ## The architecture drift problem Traditional workflows quietly split two truths: ```text Traditional Code ──────── Architecture diagram ↑ often drifts ``` - Code changes in pull requests - Architecture explanations change in meetings - Diagrams change — if they change at all — in a separate tool - Agents and new teammates inherit whichever snapshot they find first ## Why separated diagrams hurt more in the agent era When humans alone owned implementation, a stale diagram was annoying. With coding agents, it becomes expensive. An agent that only sees the current file can still miss service boundaries, dependency constraints, and the decisions that explain why the system looks the way it does. That is a [context engineering](/context-engineering) problem as much as a documentation problem: architecture is part of the information environment the agent needs — if it can find it in a usable form. A PNG in a slide deck is almost invisible to that loop. A project document next to the code can participate. This is also why architecture work on Drawsy AI sits on the [AI system architecture](/use-cases/system-architecture) path and the [developer workspace](/use-cases/developers) path — not as a one-off export from a whiteboard session. ## What DRAW.md is **DRAW.md is Drawsy AI’s canonical project document for architecture that needs to stay close to the repository.** It lives with the code as a readable project document. From there, it can render into editable canvas content on a Drawsy AI board — so the visual map is not a dead screenshot of something that used to be true. Once it is on the canvas, that architecture sits in the same workspace as connectors, decisions, and agent workflows. Coding agents can work from that visual + project layer instead of reconstructing intent from a detached image. The point is not another markdown format to maintain for its own sake. It is a bridge so architecture does not live outside the project. - Architecture stays close to the code that implements it - Canvas content can stay aligned with a readable project document - Agents can work from the same map — not a screenshot in a slide deck - Complements Git and docs; does not replace them as source of truth ## Repository ↔ DRAW.md ↔ canvas ↔ agent The intended loop is not “generate a prettier diagram.” It is to keep architecture in the middle of the development workspace: ![System architecture on a Drawsy AI canvas — the visual side of a DRAW.md-connected workflow](https://app.drawsyai.com/assets/use-cases/system-architecture.jpg) ```text DRAW.md Repository ↕ DRAW.md ↕ Drawsy AI canvas ↕ AI / Agent ``` ### What each hop is for **Repository** — where the system actually changes. Source of truth for implementation stays in Git. **DRAW.md** — the project-local architecture document that can travel with the repo and render into the canvas. **Drawsy AI canvas** — the editable visual workspace where services, flows, and decisions stay visible beside connected context. **AI / agent** — coding agents and workspace AI that can use that shared map when implementing or questioning the design — including local workflows through [Drawsy Companion](/companion). ## Where DRAW.md fits in a developer architecture workflow DRAW.md is most useful when architecture is treated as ongoing project work — not a kickoff artifact. A practical loop looks like this: - Map or update the system on the canvas with repository context nearby - Keep the architecture document in the project so it can render back into editable canvas content - Use that shared layer when asking agents to implement, refactor, or explore options - Continue past the diagram into code and previews when the design needs to meet reality ## Keeping architecture documentation synced with code DRAW.md does not magically rewrite itself every time someone merges a PR. Sync is a workflow problem, not a marketing slogan. What it changes is the default place architecture lives: - Closer to the repository than a slide deck - Renderable into an editable canvas instead of frozen as an image - Available in the same workspace agents already use for implementation context ## What DRAW.md is not DRAW.md is deliberately not a claim that Drawsy AI replaces your documentation stack or your cloud console. - It is not a replacement for Git as source of truth - It is not a replacement for ADRs, RFCs, or product specs when those already work - It is not a replacement for infrastructure-as-code or the AWS console - It is not “architecture as a generated image you cannot edit” ## Architecture as code — adjacent, not identical Teams often talk about “architecture as code” when they want diagrams and decisions versioned next to implementation. DRAW.md sits in that territory without pretending every architectural decision should become a build artifact. It is architecture documentation that can stay next to the code and become editable canvas geometry — a developer architecture workflow, not a new compile target. If you want the full system-architecture product story — existing systems, AWS maps, agents, and live previews — that lives on [AI System Architecture Tool](/use-cases/system-architecture). This article owns the DRAW.md rationale. ## Who this is for DRAW.md is for teams that already feel the gap between the diagram and the repo: - Developers who need architecture to survive past the kickoff meeting - Teams using coding agents that need better project context than a chat paste - Architects who want an editable map that can stay connected to implementation work - Anyone tired of “the real architecture is in someone’s head / last quarter’s deck” ## Related reading For the architecture product surface, see [AI System Architecture Tool](/use-cases/system-architecture). For the broader developer workflow, see [AI Workspace for Developers](/use-cases/developers). For the agent layer, see [AI Coding Agents](/ai-coding-agents). For how context should be selected and structured, see [Context Engineering](/context-engineering). For how local agents meet the canvas, see [How to Use a Local AI Coding Agent with an AI Canvas](/blog/local-ai-coding-agent-ai-canvas). > **TL;DR:** Architecture diagrams drift when they live outside the repository. DRAW.md is Drawsy AI’s bridge for keeping architecture close to the code: a project document in the repo that can render into editable canvas content and stay available to coding agents working from the same workspace. It complements Git and docs — it does not replace them as source of truth. ## Frequently asked questions ### What is DRAW.md? DRAW.md keeps architecture close to the code: a project document in the repository that can render into editable Drawsy AI canvas content and stay available to coding agents working from that workspace. ### Does DRAW.md replace Git or documentation? No. It complements Git and docs. Implementation source of truth stays in the repository; DRAW.md is a bridge so architecture does not live only as a detached diagram. ### How does DRAW.md help AI coding agents? Agents need architecture as usable project context, not a PNG in a slide deck. DRAW.md helps keep that map next to the repo and on the same Drawsy AI workspace agents can work from. ### Is DRAW.md the same as architecture as code? It sits in the same problem space — keeping architecture close to implementation — without claiming every diagram must become a build artifact. It is a readable project document that can become editable canvas content. ### Where should I read the full architecture product story? Use the AI System Architecture Tool page for existing systems, AWS infrastructure maps, agents, and live previews. This article focuses on why DRAW.md exists. ## Related reading - [/blog/from-architecture-diagram-to-running-software](/blog/from-architecture-diagram-to-running-software) - [/use-cases/system-architecture](/use-cases/system-architecture) - [/use-cases/developers](/use-cases/developers) - [/ai-coding-agents](/ai-coding-agents) - [/context-engineering](/context-engineering) - [/blog/local-ai-coding-agent-ai-canvas](/blog/local-ai-coding-agent-ai-canvas) - Start on the canvas: https://drawsyai.com --- # The AI Workspace Is Changing: From Chatbots to Context, Canvases, and Agents > AI is moving beyond chat. Explore how AI workspaces combine context, visual canvases, connected tools, and agents to turn scattered ideas into real work. ## Real work is not a conversation For the last few years, the default AI interface has been simple: a text chat box. You type. It answers. You ask again. That loop is useful. It is also incomplete. A software project is not a conversation. Neither is a product launch, a research program, a design system, or building a company. Real work is a pile of **context, decisions, files, people, tools, relationships, plans, and actions**. That is why the next wave of AI products looks less like chatbots and more like **workspaces** — places where context stays visible, canvases show structure, and agents can act without forcing every idea through another thread. ## The problem with the AI chat interface Picture a new product. You might have customer interviews, requirements, screenshots, competitor research, Figma files, architecture diagrams, GitHub repos, Slack threads, docs, spreadsheets, analytics, roadmap items, code, tasks — and a stack of AI chats. All of that matters. Your assistant usually sees only what you paste in. So the workflow becomes a human integration layer: *[Visual diagram: fragmented-chat-copy]* - The model may be strong. - The workspace around it is still fragmented. - That fragmentation is the productivity tax. ## AI is moving from answers to action Early generative AI mostly produced answers: write this, explain that, summarize this, generate some code. The next phase is different. Systems increasingly work with tools, files, apps, and external context. Instead of “tell me how to fix this,” you can ask: “understand the project, find the problem, plan the fix, implement it, and test it.” That is the shift from an **AI assistant** toward an **AI agent** — systems that can inspect repositories, call tools, run tests, debug, and patch. Coding agents are becoming a primary development interface, not only autocomplete. See also: [what AI coding agents are](/ai-coding-agents) and [how local agents meet an AI canvas](/blog/local-ai-coding-agent-ai-canvas). ## Agents create a new problem: context More capability does not automatically mean better work. An agent still needs to know what you are building, why, what was already decided, which constraints matter, which files count, which tools to use, what to change, and what to leave alone. That is why **context** — and [context engineering](/blog/context-engineering-for-ai-agents) — matters more than ever. The prompt is one piece. The **workspace** is the larger design. ## From prompt engineering to context engineering Compare two requests. **Request A:** “Build authentication.” That is a prompt. **Request B:** requirements + architecture + schema + user flows + design + existing auth code + constraints + tests → then build authentication. That is context. The second system does not always need a smarter model. It needs **better information around the task**. So the useful product question is not only “how smart is the model?” It is also: **how well does the workspace help the model understand the work?** *[Visual diagram: prompt-vs-context]* ## This is where the canvas becomes important Chat is linear. A canvas is spatial. That sounds small. It is not. A conversation forces sequence: message → response → message → response. A canvas can hold relationships: research feeding requirements, architecture branching into design and APIs, implementation connecting back to the project. You can see structure, move pieces, connect them, group them, compare them, and ask AI to work with what is already on the board — the core idea of an [AI canvas](/ai-canvas). *[Visual diagram: chat-vs-canvas]* ## AI canvases are becoming a real interface This is not only a Drawsy thesis. [Google’s Canvas in AI Mode](https://blog.google/products-and-platforms/products/search/ai-mode-canvas-writing-coding/) is positioned as a space for longer projects and plans, including coding and interactive tools. [Sheets Canvas](https://workspaceupdates.googleblog.com/2026/08/use-google-sheets-canvas-to-visualize-data.html) turns spreadsheet data into interactive visual apps. [GitHub has described canvases](https://github.blog/ai-and-ml/github-copilot/how-to-build-interactive-experiences-with-canvases/) as useful when work does not fit a linear conversation — when people need to visualize, combine sources, and act. Other products are putting coding agents onto visual agent canvases for local workflows. The direction is consistent: **AI is moving from a place you talk to into a place where you work.** ## What is an AI workspace? A useful definition: > **An AI workspace is the environment where your context, tools, AI agents, and work come together.** In practice it usually includes: *[Visual diagram: ai-workspace-layers]* - **Context** — docs, notes, research, requirements, conversations - **Visual thinking** — diagrams, flows, maps, plans, relationships - **Connected tools** — GitHub, Notion, Slack, Drive, Jira, Gmail, and more - **AI** — models and agents that can read the workspace - **Execution** — code, prototypes, tasks, plans, outputs - **Local environment** — files, projects, terminals, developer tools ## From scattered tools to one context Most companies already have useful systems. The pain is the gap between them. *[Visual diagram: scattered-tools]* - Drawsy’s starting point is simple: **your work already exists**. - Bring context together instead of rebuilding it inside every AI conversation. - When the pieces connect, AI gets closer to the environment humans already work in. ## One canvas. Many ways to think. A canvas is not only “diagramming.” The same surface can hold different kinds of work: - **Systems** — architecture, APIs, infrastructure - **Knowledge** — notes, docs, reference - **Research** — sources, findings, comparisons - **Planning** — roadmaps, tasks, priorities - **Design** — flows, wireframes, exploration - **Apps** — prototypes, code, interactive work - **Presentations** — stories, reports, communication ## From scattered context to real work A strong AI workspace helps work move forward — not just produce longer chats. *[Visual diagram: progress-not-chat]* - The goal is not longer AI conversations. - The goal is **progress**: scattered → understood → structured → action → real work. ## Then comes the agent A canvas alone is not enough. You still need execution. Humans hold direction, judgment, and creativity. Agents can explore, implement, and iterate on scoped tasks — with the board as shared context. That is a healthier division of labor than pretending either side owns the whole job. *[Visual diagram: visual-dev-workflow]* ## Why local AI agents matter For developers, the workspace gets more interesting when it can reach the machine you already use: project files, Git, terminal, coding agent. You should not need a brand-new hosted environment just to get AI help. Connect the visual workspace to the environment you already trust. That is the idea behind [Drawsy Companion](/companion). ## Drawsy Companion: canvas meets your machine Companion is a local runtime that connects the Drawsy web canvas to supported coding agents and tools on your computer. You keep Codex CLI **or** OpenCode CLI — one is enough. Companion bridges the visual workspace to the local project. Details and setup: [Companion download](/companion#download) and [local AI coding agent + AI canvas](/blog/local-ai-coding-agent-ai-canvas). *[Visual diagram: companion-bridge]* ## Your files should remain your files Local-first tooling should not force a full project upload just to get help. A local workflow can let you choose a project on disk and let the local runtime mediate access. Important precision: **local runtime does not automatically mean the AI model is offline.** Whether prompts or code reach an external model depends on the agent, provider, and configuration. Good products make that data path understandable — not just a vague “private” badge. Least-privilege access and human approval for sensitive tool actions remain good practice for agent systems. ## The workspace is the missing layer Put the pieces together and the picture is not “another chatbot,” “another editor,” or “another project tool.” It is a layer that connects **thinking → context → AI → execution.** *[Visual diagram: missing-layer]* ## What this means for different people An AI workspace should not be developer-only. The same canvas can support different workflows: - **Developers** — architecture → code → tests → debugging → deploy - **Product managers** — feedback → problems → opportunities → features → priorities → roadmap - **Researchers** — sources → evidence → patterns → insights → recommendations - **Designers** — brief → research → flows → wireframes → UI → prototype - **Founders** — idea → market → customer → product → technology → launch ## The next generation of AI products won’t just answer The most interesting products will not only win on context-window size or model brand. They will answer: **what should AI be able to understand, see, access, and act on?** That is a workspace problem — context, structure, tools, agents, and human control for decisions that matter. Together, AI starts to feel less like a chatbot and more like a collaborator inside the work. ## From chat interface to work interface The evolution looks roughly like this: *[Visual diagram: interface-evolution]* - Chat does not disappear. - Chat becomes **one interface inside the workspace**, not the whole workspace. ## The future may be less about prompting alone Prompt craft still helps: clearer asks, examples, formats. As agents take actions, another skill rises: **context design** — what the AI should know, access, remember, use, and when a human must approve. Those questions matter once AI moves from generating text to changing real systems. ## The real AI productivity problem AI can make individuals faster. If the surrounding workflow stays fragmented, the gains leak into context switching. You can generate code quickly and still spend an hour hunting the right requirement, the latest design, the customer ask, the architecture decision, the current doc version, the agent’s diff, and the review checklist. The next leap is not only “generate more output.” It is **reduce the distance between context and action.** ## Where Drawsy fits Drawsy is built around one line: **one canvas, many ways to think.** Bring work together. Connect tools. Organize context. Let AI understand it. Use agents when work needs to happen. Keep humans in charge of decisions that matter. For developers, Companion extends that workspace to the local machine with Codex CLI or OpenCode. For teams, connectors pull external context onto the board. For product, research, planning, design, and systems work, the canvas is the visual layer for relationships. The result is not only an AI that answers questions. It is a workspace designed to help turn **ideas into real work.** Start on the canvas at https://drawsyai.com, explore [how to start](/how-to), or open the [AI workspace](/ai-workspace) definition. ## The next interface for AI may be the workspace AI can increasingly understand repos, use tools, edit files, prototype, analyze data, work with connected apps, operate coding environments, and collaborate with humans. Interfaces are evolving from linear chat toward visual, persistent workspaces — which is why Google, GitHub, and others are investing in canvas-style experiences. **Canvas** gives structure. **Connectors** give access. **Agents** give action. **Humans** give direction and judgment. That combination is the foundation of the AI workspace — and where this category is heading. > **TL;DR:** Chat is useful, but real work is not a conversation — it is context, decisions, tools, and action. AI workspaces combine visual canvases, connected context, and agents so AI can understand the work instead of only answering prompts. Drawsy is that layer: one canvas for thinking, connectors for context, agents for execution, and Companion for local coding workflows. Chat does not disappear — it becomes one interface inside the workspace. ## Frequently asked questions ### What is an AI workspace? An AI workspace is a digital environment that combines AI with the context, information, tools, and workflows needed for real work — a persistent place to organize information and take action, not only a chat thread. ### What is an AI canvas? An AI canvas is a visual workspace where people organize information, ideas, plans, relationships, and interactive outputs while working with AI. ### What is the difference between an AI assistant and an AI agent? An AI assistant typically generates or transforms information in response to requests. An AI agent can use tools and perform multi-step actions toward a goal. ### What is context engineering? Context engineering is designing the information, tools, permissions, and environment available to an AI system so it can perform a task effectively. ### What is a local AI coding agent? A local AI coding workflow lets an agent work with a development environment and project resources on your computer. The model itself may still be hosted remotely depending on agent and configuration. ### What is Drawsy Companion? Drawsy Companion is Drawsy’s local runtime for connecting the Drawsy web canvas to supported coding agents and local development resources. ### Can Drawsy work with Codex or OpenCode? Yes. Drawsy Companion connects the Drawsy canvas with supported local coding agents such as Codex CLI and OpenCode CLI. ### Do I need both Codex and OpenCode? No. One supported coding agent is enough. ## Related reading - [/ai-workspace](/ai-workspace) - [/ai-canvas](/ai-canvas) - [/context-engineering](/context-engineering) - [/blog/context-engineering-for-ai-agents](/blog/context-engineering-for-ai-agents) - [/blog/local-ai-coding-agent-ai-canvas](/blog/local-ai-coding-agent-ai-canvas) - [/companion](/companion) - Start on the canvas: https://drawsyai.com --- # How to Use a Local AI Coding Agent with an AI Canvas > Learn how local AI coding agents work with Codex CLI and OpenCode, why local project access matters, and how an AI canvas can turn code, context, and ideas into one visual workspace. ## The code is connected. The rest of the work usually isn’t. AI coding agents changed what “asking for help” looks like. Instead of copying a snippet from chat into an editor, you can point an agent at a project and ask it to understand the codebase, plan a change, edit files, run tools, and iterate when something breaks. That part is getting better quickly. What still feels messy for a lot of developers is everything around the code: requirements in one doc, architecture in someone’s head, research in browser tabs, tickets in another tool, and the agent conversation somewhere else entirely. A useful pattern is emerging: keep a **local AI coding agent** close to your files, and put the thinking work on an **AI canvas** so context stays visible instead of buried in a thread. This guide explains that workflow — what local coding agents are, how Codex CLI and OpenCode fit, where a canvas helps, and how Drawsy Companion bridges the two without forcing your whole project into a hosted IDE. ## What is an AI coding agent? An AI coding assistant usually helps you write or explain code. An **AI coding agent** goes further. It can work through a multi-step development task using tools in its environment — not just generate a reply. Ask something like “understand this project and add authentication,” and a capable agent may need to: - Inspect the project structure - Find the existing application architecture - Read relevant files - Decide where authentication belongs - Create a plan - Modify multiple files - Run commands or tests - Review the result and iterate on errors ### Assistant vs agent Terminal-based agents matter because they sit closer to where you already keep source control, builds, and local tooling. The short distinction: - **Assistant:** “Here is code you could write.” - **Agent:** “I can work through the project and take the steps required to make the change.” ## What is a local AI coding agent? A **local AI coding agent** is a coding agent that operates through tools and files on your computer, rather than requiring the whole development loop to live inside a hosted browser environment. “Local” is easy to over-claim, so be precise. It does **not** automatically mean the AI model itself runs entirely offline on your machine. *[Visual diagram: local-agent-machine]* - Your project files, Git repo, terminal, and agent CLI can stay on your machine. - The agent may still call a hosted model or a local model, depending on how you configure it. - **Runs locally** and **the model runs locally** are not the same claim. ## Codex CLI and OpenCode Two practical examples of this style of workflow are [Codex CLI](https://developers.openai.com/codex/) and [OpenCode](https://opencode.ai/). Codex is built to operate as a coding agent in development environments. OpenCode is an open-source AI coding agent available through terminal, desktop, and IDE-style experiences, with support for multiple providers and local models — useful when you care about how the agent environment is configured. You do not need both. For a Drawsy Companion setup, the relationship looks like this: *[Visual diagram: companion-bridge]* - Install the Companion on your machine. - Install **either** Codex CLI **or** OpenCode CLI. - Connect a local project from the Drawsy canvas. - One supported agent is enough. ## Why put an AI coding agent on a canvas? A terminal is excellent for execution. An editor is excellent for writing and reviewing code. Chat is excellent for short questions. Software projects are rarely just code. They also include architecture, product requirements, user flows, research notes, screenshots, API docs, database models, bugs, tasks, and design decisions. Trying to hold all of that in a linear conversation gets expensive — for you and for the agent. An [AI canvas](/ai-canvas) changes the interaction model. Instead of another prompt stacked on another reply, you can keep the structure of the work visible. *[Visual diagram: chat-vs-canvas]* - The canvas holds **context and structure**. - The coding agent can **act** on that context against a real project. - This pattern is showing up beyond Drawsy — for example, [Google’s canvas experiences for planning and coding](https://blog.google/products-and-platforms/products/search/ai-mode-canvas-writing-coding/) and [GitHub’s guidance on canvases for work that does not fit linear chat](https://github.blog/ai-and-ml/github-copilot/how-to-build-interactive-experiences-with-canvases/). ## The problem with the traditional AI coding workflow Picture an e-commerce feature. Feedback lives in Slack. Requirements sit in Notion. Design is in Figma. Code is in GitHub. The agent runs in a terminal. The architecture discussion happened in yet another chat window. You become the integration layer. That creates context switching: read a requirement, open the repo, re-explain the goal to the agent, paste a diagram somewhere else, run the agent, jump back to docs, return to the project. The agent can be strong and the surrounding workflow can still be fragmented. *[Visual diagram: fragmented-workflow]* ## A visual AI development workflow With a visual workspace, the same project can stay connected as one map instead of six tabs. *[Visual diagram: visual-dev-workflow]* - You can see how requirements relate to architecture and implementation. - The canvas does not replace the coding agent. - It gives the agent — and you — a clearer workspace to operate from. ## Where Drawsy Companion fits This is the idea behind [Drawsy Companion](/companion). Companion is a lightweight local runtime that connects the Drawsy web canvas to tools already installed on your computer. It is the bridge — not the model, and not a replacement for Codex or OpenCode. In practice: Drawsy stays in the browser for visual work. Companion runs on your machine. A supported local coding agent works with the project folder you choose. That combination is the product claim worth being clear about: Drawsy is the visual workspace around your AI coding agent. Companion is how that workspace reaches your local environment. *[Visual diagram: companion-bridge]* ## What the local workflow looks like The setup matches what we document on the Companion page: - **1. Install Drawsy Companion** for Windows, macOS, or Linux — see [download](/companion#download). - **2. Install one coding agent:** [Codex CLI](https://learn.chatgpt.com/docs/codex/cli) or [OpenCode CLI](https://opencode.ai/docs/#install). One is enough. - **3. Start Companion** so it can run in the background. - **4. Open Drawsy** in the browser. - **5. Connect your project** and choose the available agent. - **6. Start building** with the canvas and local tools together. ## What you can actually do with an AI canvas The interesting part is not drawing boxes for their own sake. The canvas can represent the state of the work. Start with a product idea — “build a marketplace for independent designers” — then grow connected context: personas, user journey, feature requirements, database model, API architecture, technical plan. From there, a coding agent can work against the resulting context instead of treating each stage as an isolated chat. Idea → research → architecture → plan → code becomes one continuous workspace. ## AI canvas use cases for developers A few patterns show up repeatedly: ### 1. System architecture Map frontend, API, auth, database, and storage, then connect implementation details back to the actual project files the agent can touch. ### 2. Product planning Turn customer feedback → problems → opportunities → features → priorities into a visual board. Useful when founders and PMs need the product story sitting next to implementation. ### 3. Research Collect documentation, notes, links, screenshots, and alternatives into a research map instead of a bookmark pile. ### 4. Debugging Keep the investigation visible as it develops, rather than trapping it inside one long chat transcript. *[Visual diagram: debugging-canvas]* ### 5. Technical design Requirement → architecture → components → APIs → database → implementation gives humans and agents a shared picture of the system. ### 6. Working with AI agents A more advanced workflow treats the canvas as a control surface for agentic work — research, planning, implementation, tests, review — without pretending the canvas replaces developers. *[Visual diagram: agentic-control-surface]* ## Does local mean my data never leaves my computer? No — not automatically. Treat that claim carefully. A modern AI development workflow usually has several pieces: your local project, Companion, the coding agent, the AI model or provider, and any external connectors. A local-first setup can keep project files and the local bridge on your machine. Whether prompts or source code are sent to an external model depends on the agent, the provider, your configuration, and the action being performed. OpenCode, for example, supports hosted providers and local models, so the data path depends on setup. Do not treat “local AI” as a synonym for “offline AI.” ## Do I need a Drawsy subscription? For Companion’s local workflow: you can use the Drawsy canvas with a local coding-agent setup **without a Drawsy subscription**. That is not the same as “free AI coding with no account anywhere.” You still need a supported coding agent and whatever model or provider access that agent requires. Companion connects Drawsy to that agent — it does not replace model access. ## AI canvas vs AI chat Chat is great for “explain this function.” A canvas is better when the question is “here is the whole problem — how do these pieces connect?” As agents take on larger workflows, the shift looks less like better prompts and more like better workspaces: conversation → workspace, prompt → task, chat history → persistent project context. Google has described canvas as a place for plans and coding work; GitHub has made a similar point that some tasks need visualization and multi-source action rather than a long linear thread. ## What the workspace around an agent should look like Developers will keep using terminals, IDEs, Git, browsers, docs, issue trackers, and agents. The opportunity is making those systems share context instead of forcing everything through one interface. An AI coding agent is powerful because it can act. A canvas is powerful because it can organize complexity. Together, you get a visual workspace where humans can understand the work and agents can help execute it. The interesting question is no longer only “can AI write code?” It already can. The better question is: **what should the workspace around an AI coding agent look like?** ## Try the workflow If you already use Codex CLI or OpenCode, you do not need to throw away your project, Git habits, or local tools. Add the canvas layer. Think visually. Bring in context. Let the agent work against the project on your machine. Open the canvas at https://drawsyai.com, or [download Drawsy Companion](/companion#download) and follow the [setup steps](/companion#setup). For related concepts, see [AI coding agents](/ai-coding-agents) and [context engineering for AI agents](/blog/context-engineering-for-ai-agents). > **TL;DR:** An AI coding agent can work directly with a software project using tools such as file access and terminal commands. A visual AI canvas adds a persistent workspace for requirements, architecture, research, and implementation context. Drawsy Companion connects the Drawsy web canvas to a supported coding agent — Codex CLI or OpenCode — running on your computer. You only need one agent. Local project access does not automatically mean the model itself runs offline. ## Frequently asked questions ### What is a local AI coding agent? A local AI coding agent operates with a development environment and project resources available on your computer. The exact data flow still depends on the agent and model provider configuration. ### What is an AI coding agent? An AI coding agent can perform multi-step software tasks using tools such as file access, terminal commands, code editing, and project analysis — not only generate snippets in a chat reply. ### What is Codex CLI? Codex CLI is OpenAI’s command-line coding agent for working with software projects in a development environment. ### What is OpenCode? OpenCode is an open-source AI coding agent available through terminal, desktop, and IDE experiences, with support for multiple model providers and local models. ### Can I use Codex or OpenCode with an AI canvas? Yes. A local bridge such as Drawsy Companion can connect a web-based visual workspace to a coding agent running on your computer. ### Do I need both Codex and OpenCode? No. One supported coding agent is enough. ### What is an AI canvas? An AI canvas is a visual workspace for organizing ideas, information, plans, diagrams, and other project context while working with AI — especially useful for tasks that do not fit neatly into linear chat. ### Is an AI canvas the same as an IDE? No. An IDE is primarily for writing, running, and debugging code. An AI canvas sits at a broader level: product thinking, research, architecture, planning, and implementation context. ### Is Drawsy Companion an AI model? No. Drawsy Companion is the local bridge/runtime. It connects the Drawsy web canvas with supported local coding agents and local development resources. ### Does Drawsy Companion install Codex or OpenCode? No. You install your preferred coding agent separately. Companion connects Drawsy to the agent already on your machine. ## Related reading - [/blog/ai-workspace-context-canvas-agents](/blog/ai-workspace-context-canvas-agents) - [/companion](/companion) - [/companion#download](/companion#download) - [/companion#setup](/companion#setup) - [/ai-coding-agents](/ai-coding-agents) - [/ai-canvas](/ai-canvas) - [/#use-cases](/#use-cases) - [/blog/context-engineering-for-ai-agents](/blog/context-engineering-for-ai-agents) - Start on the canvas: https://drawsyai.com --- # Context Engineering for AI Agents: How to Give AI the Right Information > A practical guide to context engineering for AI agents — how to design instructions, knowledge, memory, tools, and feedback so agents make better decisions on real projects. ## Strong models still fail with weak context Most people notice agent failures as “the model hallucinated” or “it wrote bad code.” Often the real issue is quieter: **The agent did not have the information a careful engineer would have checked first.** Stale docs. Wrong files. A truncated repo view. No record of why the team rejected an approach two years ago. Tool output dumped in without meaning. Any of that can push a capable model into a confident wrong turn. [Context engineering](/context-engineering) is the craft of designing that information environment — not polishing one magic prompt. The goal is not to feed the agent everything. The goal is to feed it **what matters for this task**. ## What is context engineering? Context engineering means deciding **what an AI agent should see, how that information should be structured, and when it should arrive**. A toy agent can look almost trivial: *[Visual diagram: simple-agent]* ### What a real project agent needs On a real product, the model is only one part of the system. The surrounding stack — project context, memory, tools, documents, decisions, tool results — decides what the model can actually reason about. *[Visual diagram: agent-context-stack]* ## Context engineering vs prompt engineering Prompt engineering asks: **how should I phrase the instruction?** Example: “Review this function and explain the bug.” Context engineering asks: **what should the model already know before it reviews that function?** Often that means callers, schema, API contracts, tests, recent commits, architecture decisions, error logs, project conventions, and the user’s real goal. Prompts still matter. For multi-step agents, the context pipeline becomes system design. | Prompt engineering | Context engineering | | --- | --- | | How should I instruct the model? | What should the model know? | | Focuses on wording | Focuses on the information environment | | Usually one interaction | Can span a whole workflow | | Prompt text | Retrieval, memory, tools, state, instructions | | "What should I ask?" | "What should the agent access?" | ## Why context matters more for AI agents A chatbot can sometimes answer from general knowledge. An agent has to pursue a goal: inspect, choose a tool, act, read the result, update the plan, act again, validate. Every step creates or consumes context. Ask a coding agent: “Add authentication.” That sentence is not enough to do the work safely. It needs the repo shape — frontend, backend, database, auth, routes, tests, env, deploy — plus which provider you already use, how sessions work, which routes are protected, security conventions, deploy constraints, and past architecture decisions. Without that, you get code that looks fine and still breaks the system. That is a common failure mode for [AI coding agents](/ai-coding-agents) that only see a thin slice of the codebase. *[Visual diagram: repo-tree]* ## Five layers of useful agent context There is no single universal architecture. These five layers are a practical map when you are designing agent systems — or when you are trying to debug why an agent went sideways. ### 1. Instructions Role, task, constraints, output shape, safety rules, project conventions. For a coding agent, that can look like: ```text Follow the existing TypeScript conventions. Do not introduce a new authentication library. Run the existing test suite after changes. Do not modify database migrations unless required. ``` - Instructions set boundaries. They are not the whole project truth. ### 2. Knowledge Docs, repos, APIs, databases, files, search results, retrieved notes — anything that describes the world the agent is changing. **Relevance beats volume.** Dumping the whole knowledge base into the window rarely helps. The system has to find what matters for this task. That is the core of [connected context](/connected-context). ### 3. State and memory Some facts should survive across runs. Example: the team already chose PostgreSQL because analytics depends on it. That one decision is often more valuable than another generic explanation of PostgreSQL. Good memory keeps what stays useful — not everything that ever happened. *[Visual diagram: decision-memory]* ### 4. Tools GitHub, databases, APIs, filesystems, browsers, terminals, deploys, observability — tools expand what an agent can do. They also expand what the agent must understand: when to use a tool, what inputs it accepts, what the output means, and what permissions are allowed. A powerful tool with weak context still produces weak results. ### 5. Execution feedback After the agent acts, reality talks back: failing tests, error logs, lint output, diff review notes. That feedback should enter the next step’s context. Long-running agents need this loop more than chatbots do. *[Visual diagram: feedback-loop]* ## More context is not always better Suppose you ask an agent to fix a login bug and hand it the entire repo, every issue, a year of Slack, all logs, every deploy, the full docs site, and hundreds of old chats. Technically that is **more** context. Practically it is often a worse problem: noise, stale claims, contradictions, duplicates, and cost. A tighter pack — login service, auth middleware, related model, recent auth commit, failing test, current error — is smaller and usually stronger. Context engineering is not “max out the window.” It is **select and structure the right context.** ## Context engineering for coding agents Software hides decisions. A teammate may know: “We don’t use that service here — it raced two years ago.” That fact might live in an old PR, an ADR, Slack, a comment, or someone’s head. The current file may never say it. An agent that only reads today’s code can miss the constraint and “improve” the system in the wrong direction. A stronger coding-agent context looks more like a pipeline than a paste dump: *[Visual diagram: coding-agent-pipeline]* - Do not expose the entire history of the project by default. - Expose the history that explains why the system looks the way it does now. ## A practical workflow you can use today If you are building or steering an agent, start with the task — not the model brand. ### Step 1 — Define the task Make the goal specific enough to verify. Weak: “Improve the application.” Stronger: “Add pagination to `/api/projects` without changing the existing response shape.” ### Step 2 — Identify required context Ask what an experienced engineer would open before editing: ```text API implementation Database query Existing response type Frontend consumer Tests Pagination conventions ``` ### Step 3 — Retrieve only what is relevant Skip “load everything.” Prefer semantic retrieval, keyword search, metadata filters, repo structure, dependency edges, and task-specific routing. ### Step 4 — Separate facts from instructions Agents handle mixed soup poorly. Keep facts, constraints, and the task distinct so the model can weight them correctly. *[Visual diagram: facts-constraints]* ### Step 5 — Preserve important decisions When a decision matters, store the decision, why it was made, what it affects, and what it replaces. Future agents should not re-litigate settled architecture every run. ### Step 6 — Validate, then feed the result back Generation is not the end. For engineering work: plan → implement → test → inspect failures → update context → fix → validate. The validation result becomes context for the next move. ## Treat context as a pipeline A useful mental model for many AI apps: **Context is a pipeline, not a text box.** *[Visual diagram: context-builder]* ## How to tell if your context is good Before you send a pack of context to an agent, run a fast checklist: - **Relevant?** Does this help with the current task? - **Sufficient?** Can a careful engineer decide with this information? - **Current?** Could any of it be outdated? - **Consistent?** Are sources or instructions fighting each other? - **Traceable?** Do you know where the critical facts came from? ## Bigger models do not replace better context As models improve, more of the hard work moves outward — from “how smart is the model?” to “what did we give it to work with?” A strong model with poor context still makes bad calls. A weaker model with sharp context can often outperform it on a real codebase. That is why context engineering belongs next to model choice when you care about reliable agents. ## Where AI workspaces help An [AI workspace](/ai-workspace) is one way to keep the right pieces next to each other: ideas, docs, code links, decisions, diagrams, and agent work — instead of scattering them across Slack, Notion, GitHub, whiteboards, and chat. The point is not “give AI more stuff.” It is make relationships visible so both humans and agents can use them. On Drawsy, that often looks like idea → architecture → documentation → code → decisions → AI → implementation on one board. If you are pairing that visual layer with local coding agents such as Codex CLI or OpenCode, read [how to use a local AI coding agent with an AI canvas](/blog/local-ai-coding-agent-ai-canvas). Try the canvas at https://drawsyai.com, skim [how to start](/how-to), or see [Drawsy agents](/agents) when you want AI that works with the board — not beside it. *[Visual diagram: workspace-flow]* ## Takeaway Context engineering is not the hunt for a perfect prompt. It is designing the **information environment around an AI system**. For agents, that environment usually includes instructions, project knowledge, retrieval, memory, tools, tool results, constraints, and execution feedback. **Give the agent what it needs, when it needs it, in a form it can use.** As agents take on longer work, that design choice can matter as much as the model itself. > **TL;DR:** Context engineering is designing what an AI agent receives — instructions, knowledge, memory, tools, and feedback — so it has the right information, in the right form, at the right time. More context is not automatically better. Relevant, current, consistent context is. Coding agents fail often when they see files but miss decisions, conventions, and constraints that live outside the current diff. ## Frequently asked questions ### What is context engineering? Context engineering is deciding what information an AI agent should receive, how it should be structured, and when it should be provided — including instructions, knowledge, memory, tools, and execution feedback. ### How is context engineering different from prompt engineering? Prompt engineering focuses on how you instruct the model. Context engineering focuses on the information environment around the model: retrieval, memory, tools, state, and constraints across a workflow. ### Why do coding agents need better context? Software systems carry implicit decisions — old PRs, ADRs, Slack threads, conventions — that may not appear in the current file. Without that context, agents can write plausible code that is wrong for the real system. ### Is more context always better? No. Irrelevant, stale, duplicated, or contradictory information can make the task harder. Good context engineering selects and structures what matters for the current task. ### What are the main layers of agent context? A practical map is instructions, knowledge, state/memory, tools, and execution feedback. Real systems may combine these layers differently, but each answers a different failure mode. ## Related reading - [/blog/ai-workspace-context-canvas-agents](/blog/ai-workspace-context-canvas-agents) - [/context-engineering](/context-engineering) - [/ai-coding-agents](/ai-coding-agents) - [/blog/local-ai-coding-agent-ai-canvas](/blog/local-ai-coding-agent-ai-canvas) - [/ai-workspace](/ai-workspace) - [/connected-context](/connected-context) - Start on the canvas: https://drawsyai.com --- # An AI Workspace for Developers > Bring your code, architecture, project context, and AI coding agents onto one visual workspace. Map systems on an infinite canvas, connect your project sources, and work with AI agents that can reason from the context you've chosen. ## The developer context problem Code, architecture, documentation, issues, team decisions, and AI chat usually live in different places. The codebase tells you what exists. Architecture diagrams explain relationships. Documentation captures intent. Conversations contain decisions. AI coding agents need enough of that context to work effectively — and pasting fragments into a blank prompt does not recreate the map. ```text Code + Architecture + Documentation + Issues + Team decisions + AI chat ↓ Often scattered across tools ``` ## From codebase to visual context Drawsy AI gives developers a visual layer around the tools they already use: repository context from GitHub, architecture and plans on the canvas, project knowledge from docs and conversations, and a bridge to local coding tools through Companion. ![Developer architecture and project context mapped on a Drawsy AI canvas](https://app.drawsyai.com/assets/use-systems.png) ```text GitHub ↓ Repository context Canvas ↓ Architecture + plans Notion / Slack / Drive ↓ Project knowledge Companion ↓ Coding tools ↓ Drawsy AI workspace ↓ AI / agents ``` ## The developer workflow A practical loop on Drawsy AI looks like this: ```text Map ↓ Connect ↓ Contextualize ↓ Ask / Agent ↓ Build ``` - Map the system — architecture, services, dependencies, and flows on the canvas - Connect the project — bring in relevant sources such as GitHub, Notion, or Slack - Add context — keep decisions, requirements, notes, and diagrams together - Work with AI — ask questions or run agents against the context you chose - Build — take the result into your editor, CLI, or review workflow ## What developers can use it for Real development jobs, not generic feature buckets: - Understand an existing codebase — map architecture and connect the repository - Plan a feature — combine requirements, architecture, and implementation considerations - Design a system — explore services, APIs, and dependencies visually - Prepare an AI coding task — give the agent relevant architecture and project context - Investigate a technical problem — put evidence, code references, and hypotheses on the canvas - Document decisions — keep the reasoning behind an implementation next to the resulting architecture ## AI coding agents + Drawsy AI AI coding agents can execute multi-step development tasks, but the quality of their work depends partly on the context available for the task. Drawsy AI provides a visual project layer around that workflow, connecting architecture, project information, and the coding environment. Learn what [AI coding agents](/ai-coding-agents) are and how they differ from assistants — then come back here for the developer workflow. ## Bring the canvas into your development workflow Drawsy Companion connects the Drawsy canvas with local project folders and tools such as Codex CLI and OpenCode CLI. Your project files stay on your machine; the canvas holds the spatial map of what you are building. Explore [Drawsy Companion](/companion) for installers and setup. Companion is a local bridge — whether prompts reach an external model still depends on the coding agent and provider you configure. ## Why a visual workspace? Architecture and software systems contain relationships that are difficult to understand from isolated files. A visual workspace gives developers a place to represent those relationships while keeping the underlying project context nearby. Learn about [visual thinking](/visual-thinking) if you want the broader reasoning model — this page is about applying that model to day-to-day development on Drawsy AI. ## What Drawsy AI isn't Drawsy AI isn't intended to replace your code editor, GitHub repository, documentation system, or existing development tools. It provides a visual workspace around them — so architecture, project context, and AI agents can share the same board as you build. ## Related For the category definition, see [What is an AI workspace?](/ai-workspace). For how sources become shared project context, see [connected context](/connected-context). For designing what AI sees, see [context engineering](/context-engineering). For architecture-focused workflows — including existing systems, AWS maps, and architecture → agent → build — see [AI system architecture tool](/use-cases/system-architecture). Start free: https://drawsyai.com ## FAQ ### What can developers use Drawsy AI for? Mapping architecture, planning features, preparing AI coding tasks, investigating technical problems, and keeping decisions next to the systems they describe — with connected project sources on one canvas. ### Can I connect a GitHub repository? Yes. GitHub is a core connector for developer workflows. You authorize access and link relevant repositories to the workspace so AI and humans can work from that project context. ### Can developers use AI coding agents with Drawsy AI? Yes. Drawsy AI provides a visual project layer around agent workflows, and Drawsy Companion bridges the canvas with local tools such as Codex CLI and OpenCode CLI. ### Does Drawsy AI replace my IDE? No. Drawsy AI is a visual workspace around your existing tools — editor, GitHub, docs, and coding agents — not a replacement for them. ### Can I use Drawsy AI without signing in? Yes. You can open the canvas at https://drawsyai.com in guest mode without an account. Sign in when you need to save, share, or use account features. ### What coding tools work with Drawsy Companion? Drawsy Companion currently documents bridges for Codex CLI and OpenCode CLI, plus local project folders. See the Companion page for the latest supported setup. --- # AI System Architecture Tool > Design, understand, and communicate software architecture with your project context, AI, and an editable visual workspace. Connect your codebase and relevant sources, map services and dependencies on the canvas, work with AI and coding agents, and turn architecture decisions into working software. ## Architecture is more than a diagram A system architecture diagram is only useful when it reflects the system behind it. Isolated drawings drift. Screenshots rot. The useful map stays connected to code, requirements, dependencies, infrastructure, and the decisions that shaped them. Drawsy AI brings those layers together on one visual workspace. Learn how [context engineering](/context-engineering) designs what information AI should receive — this page is about using that approach for software architecture. ```text Diagram + Code + Requirements + Dependencies + Infrastructure + Decisions ↓ Useful architecture context ``` ## Understand the system you already have Connect a GitHub repository and related project sources, then map services, dependencies, and flows on the canvas. AI can help explain relationships in the context you authorize — not invent a system you do not have. ![System architecture mapped on a Drawsy AI canvas with services and dependencies](https://app.drawsyai.com/assets/use-cases/system-architecture.jpg) ```text GitHub ↓ Project context ↓ Drawsy canvas ↓ Architecture map ↓ AI explains relationships ``` - Repository and service boundaries on one board - Documentation and decisions linked beside the diagram - Questions scoped to the architecture you selected ## Design systems before you build them Sketch components, services, data flows, and dependencies while requirements are still changing. AI can help explore alternatives and structure incomplete designs — it does not make architectural decisions automatically. ```text Requirements ↓ Components ↓ Services ↓ Data flows ↓ Dependencies ↓ Architecture ``` ## AWS infrastructure → visual architecture Yes — you can use Drawsy AI to understand and design AWS infrastructure as part of the same architecture workspace, not as a detached cloud-console export. Connect AWS resource context, bring infrastructure relationships onto the canvas, and turn them into an editable architecture map you can reason about and redesign beside application design. ![AWS infrastructure and system architecture visualized on a Drawsy AI canvas](https://app.drawsyai.com/assets/use-systems.png) ```text AWS resources ↓ Infrastructure context ↓ Drawsy canvas ↓ Editable architecture ↓ Reason / redesign ``` - Infrastructure context from AWS sits on the same board as application architecture - Editable visual map — services, networks, and data stores you can rearrange - Reason about the running estate and design changes without leaving the workspace - Not a replacement for the AWS console — a visual layer for understanding and redesign ## Give coding agents the architecture behind the task Architecture without implementation context is incomplete. Implementation without architecture context is risky. Drawsy AI provides a visual project layer so supported coding workflows — including Codex CLI and OpenCode CLI via Companion — can work from selected workspace context rather than an isolated prompt. Learn what [AI coding agents](/ai-coding-agents) are, then use this page for the architecture → agent → build loop. ```text Architecture + Project context + Selected code ↓ Agent ↓ Implementation ``` ## Don't stop at the diagram Architecture is the bridge between understanding a system and building it. On Drawsy AI, the loop can continue past the diagram: agents work with project context, produce or change code, and live previews can bring running software back onto the canvas so you can refine the design against something real. For the full workflow story, see [From Architecture Diagram to Running Software](/blog/from-architecture-diagram-to-running-software). ```text Architecture ↓ Agent ↓ Code ↓ Run ↓ Live Preview ↓ Refine ``` ## Keep architecture close to the code DRAW.md is how Drawsy AI keeps architecture next to the repository — a canonical project document that can render into editable canvas content, then stay available to coding agents working from the same workspace. The point is not another markdown format to maintain for its own sake. It is a bridge so architecture does not live as a detached diagram outside the project. For the full rationale, see [Why We Built DRAW.md](/blog/why-we-built-draw-md). ```text Repository ↕ DRAW.md ↕ Drawsy canvas ↕ Coding agent ``` - Architecture stays close to the code that implements it - Canvas content can stay aligned with a readable project document - Agents can work from the same visual + project layer — not a screenshot in a slide deck - Complements Git and docs; does not replace them as source of truth ## What you can design Common architecture jobs on Drawsy AI: - System architecture — services, dependencies, and data flows - API architecture — endpoints, services, and integrations - Database architecture — schemas, relationships, and migrations - Cloud infrastructure — AWS resources and infrastructure relationships - Distributed systems — queues, services, workers, and event flows - Application architecture — frontend, backend, and external services ## What Drawsy AI isn't Drawsy AI isn't your cloud console, Git provider, IDE, or project tracker. It gives you a visual workspace where context from those systems can be brought together to understand and design the system — then continue into agents, code, and previews when you need them. ## Related For the end-to-end workflow, see [From Architecture Diagram to Running Software](/blog/from-architecture-diagram-to-running-software). For why DRAW.md exists, see [Why We Built DRAW.md](/blog/why-we-built-draw-md). For the broader developer workflow, see [AI workspace for developers](/use-cases/developers). For the category, see [AI workspace](/ai-workspace) and [AI canvas](/ai-canvas). For sources and grounding, see [connected context](/connected-context) and [context engineering](/context-engineering). For the agent layer, see [AI coding agents](/ai-coding-agents) and [Drawsy Companion](/companion). Start free: https://drawsyai.com ## FAQ ### What is an AI system architecture tool? An AI system architecture tool helps you design, understand, and communicate software architecture with visual maps plus project context — not only static diagrams. Drawsy AI is an AI workspace used for that work. ### Can Drawsy create system architecture diagrams? Yes. You can map services, dependencies, and flows on an infinite canvas, with AI helping expand or question the design from the context you authorize. ### Can I visualize an existing codebase? Yes. Connect GitHub and related sources, map the architecture on the canvas, and ask AI about relationships in the context you selected. ### Can Drawsy visualize AWS infrastructure? Yes. Connect AWS resource context, bring infrastructure onto the canvas as editable architecture, and reason about or redesign it beside application design — without replacing the AWS console. ### What is DRAW.md? DRAW.md keeps architecture close to the code: a project document in the repository that can render into editable Drawsy canvas content and stay available to coding agents working from that workspace. ### Can I connect GitHub to an architecture workspace? Yes. GitHub is a core connector for architecture workflows so repository context can sit beside the diagram. ### Can AI help design a software architecture? AI can help explore options, structure incomplete designs, and question gaps. It does not automatically make architectural decisions — humans still own trade-offs. ### Can I use coding agents with my architecture? Yes. Architecture and project context on the canvas can inform coding-agent workflows, including Codex CLI and OpenCode CLI via Drawsy Companion. ### Can I turn architecture into working software? Drawsy AI supports continuing past the diagram — agents, code, and live previews — so architecture work can feed implementation and refinement on the same workspace. ### Does Drawsy replace my architecture or diagramming tools? No. Drawsy AI is a visual workspace around your existing stack — cloud console, Git, IDE, and docs — not a replacement for them. --- # AI Product Roadmap Tool > Turn research, ideas, and priorities into a visual product roadmap — then keep that plan connected to evidence, tasks, and engineering. Drawsy AI helps product teams plan on an infinite canvas with AI and connected project sources. ## Why product roadmaps become disconnected Research lives in one place. Requirements sit somewhere else. Tasks and trackers live elsewhere. Decisions disappear into meeting notes. The roadmap becomes a slide that no longer matches the evidence or the work in progress. Drawsy AI gives product teams a visual planning workspace where research, priorities, milestones, and execution context can stay on the same board. ```text Research + Requirements + Decisions + Tasks / trackers ↓ Often scattered across tools ``` ## Turn product context into a roadmap Start from the evidence, not a blank timeline. Move from research and ideas into themes, priorities, and a roadmap the team can actually share. ![Product roadmap and planning context on a Drawsy AI canvas](https://app.drawsyai.com/assets/use-cases/product-roadmap.jpg) ```text Research ↓ Ideas ↓ Themes ↓ Priorities ↓ Roadmap ↓ Tasks ↓ Execution ``` ## Plan visually A visual roadmap makes relationships obvious: what depends on what, what ships when, and which initiatives share a theme. Keep timelines, milestones, initiatives, dependencies, and user journeys editable on one canvas — not locked in a presentation export. - Timeline and milestone views - Dependencies between initiatives - Themes and priority grouping - User journeys beside the plan ## Bring the evidence with the plan Connect the sources that justify the roadmap — docs, email, files, conversations, and meeting notes — so AI and teammates work from the same evidence, not a summary someone pasted once. - Notion — PRDs, specs, and discovery notes - Google Drive — decks, research files, and briefs - Gmail — stakeholder threads tied to decisions - Slack — conversations that changed priorities - Fireflies / Read AI — meeting notes beside the plan ## Use AI to explore and structure AI can help summarize research, group ideas into themes, spot gaps, compare priority options, and turn notes into planning artifacts. It does not automatically decide your roadmap — product judgment stays with the team. - Summarize linked research and feedback - Group ideas into themes - Identify gaps in the plan - Compare priority options against evidence on the board - Turn notes into roadmap and planning artifacts ## Move from roadmap to execution A roadmap that never reaches delivery is just a slide. Keep planning connected to how work gets done — Kanban-style boards, Jira workflows, tasks, and the engineering context that implements the plan. ```text Roadmap ↓ Kanban / Jira / tasks ↓ Architecture ↓ Developers ↓ Delivery ``` ## Keep product and engineering connected Roadmaps fail when product and engineering work from different artifacts. Keep the plan beside the systems that will ship it. See [AI workspace for developers](/use-cases/developers) for engineering workflows, and [AI system architecture tool](/use-cases/system-architecture) for mapping the systems behind the roadmap. ## What Drawsy AI isn't Drawsy AI isn't a replacement for Jira, your project tracker, or your documentation system. It doesn't automatically decide your roadmap. It gives product teams a visual workspace where research, priorities, and execution context can stay connected. ## Related For the planning surface, see [AI canvas](/ai-canvas) and [visual thinking](/visual-thinking). For how sources feed the plan, see [connected context](/connected-context). For how AI uses that evidence, see [context-aware AI](/context-aware-ai). For delivery-side workflows, see [developers](/use-cases/developers) and [system architecture](/use-cases/system-architecture). Start free: https://drawsyai.com ## FAQ ### What is an AI product roadmap tool? An AI product roadmap tool helps teams turn research, ideas, and priorities into a visual plan — with AI helping structure and explore options while humans own decisions. Drawsy AI is an AI workspace used for that planning work. ### Can AI create a product roadmap? AI can help summarize evidence, group themes, and draft planning artifacts from context you authorize. It should not silently invent priorities or replace product judgment. ### Can I connect Jira? Roadmap planning on Drawsy AI is designed to connect to execution — including task workflows and Jira where available — so the plan does not stay stranded as a slide. ### Can I use research in my roadmap? Yes. Connect docs, files, conversations, and meeting notes so research stays beside themes, priorities, and milestones on the canvas. ### Can developers work from the roadmap? Yes. Keep the roadmap connected to engineering workflows and architecture so delivery teams share the same plan. See the developers and system architecture use cases. ### Is Drawsy a project management tool? No. Drawsy AI is a visual AI workspace for planning and context. It does not replace your project tracker — it connects planning artifacts to the systems and tasks around them. ### Can I create a visual roadmap? Yes. Map timelines, milestones, dependencies, themes, and initiatives on an infinite canvas, with linked evidence and AI available beside the plan. --- # Shivam Nimbalkar > Founder, Drawsy AI Founder of Drawsy AI. Building the visual workspace around AI coding agents — local-first development, connected context, and canvas-native workflows for software teams. Expertise: AI coding agents, AI canvas, local-first development, developer workflows, context engineering Articles: https://app.drawsyai.com/blog Profile: https://app.drawsyai.com/authors/shivam-nimbalkar --- # Drawsy AI Team > Product We build Drawsy AI — a canvas that thinks with you. Visual thinking, connected context, and AI agents in one workspace. Expertise: AI workspaces, visual thinking, connected context, developer workflows Articles: https://app.drawsyai.com/blog Profile: https://app.drawsyai.com/authors/drawsy-team --- # Optional # Drawsy AI Changelog > Release notes for canvas improvements, AI, connectors, auth, and account features. ## 2026-08-23 — SEO + GEO infrastructure hardening Marketing site discovery routes are now explicit XML handlers for sitemap and RSS. Glossary pages gain visible FAQs with structured data. Compare markdown mirrors match full HTML depth. - Reliable /sitemap.xml and /blog/rss.xml generation - Glossary FAQ sections on all concept pages - Expanded changelog and synced llms-full compare content - New use-case hubs for developers, system architecture, and product roadmaps ## 2026-08-19 — Blog expansion and homepage showcase Three long-form blog posts cover AI workspaces, local coding agents with Companion, and context engineering. Homepage video showcase highlights what teams can build on the canvas. - Blog posts on AI workspace evolution, local AI coding agents, and context engineering - RSS feed at /blog/rss.xml - Author pages for Shivam Nimbalkar and Drawsy AI Team ## 2026-08-15 — Drawsy Companion launch Drawsy Companion connects local Codex CLI and OpenCode workflows to the visual canvas. Free desktop app for Windows, macOS, and Linux. - Local bridge between coding agents and canvas context - Dedicated /companion page with install guides - Companion software schema and how-to structured data ## 2026-08-10 — Compare hub and glossary pages Full comparison pages for Miro, FigJam, and Excalidraw. Seven glossary pages define AI workspace, AI canvas, context engineering, and related concepts for search and LLM discovery. - /compare/miro, /compare/figjam, /compare/excalidraw - Glossary at /ai-workspace, /ai-canvas, /context-engineering, and more - llms.txt and llms-full.txt with markdown mirrors for every page ## 2026-08-01 — Agents, pricing, and connectors marketing Public marketing surfaces for AI agents, pricing plans, help center, and connector documentation. Guest-first canvas flow documented across the site. - /agents page for grounded AI runs on the board - Public pricing at /pricing — Free, Pro, Team, Enterprise - Help center with FAQ structured data - Connector anchors for GitHub, Notion, Slack, Drive, and more