# 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. --- HTML: https://app.drawsyai.com/ai-coding-agents llms.txt: https://app.drawsyai.com/llms.txt