Definition
AI coding agents go beyond autocomplete. They can search a codebase, edit multiple files, run commands, open pull requests, and iterate toward a goal you describe. They combine language models with tools, policies, and often human approval gates.
Agents differ from chat assistants in autonomy and scope. An assistant answers a question; an agent may pursue a multi-step plan — refactor a module, add tests, or scaffold a feature — while checking its work against the repo.
Effective coding agents still need context: which service owns a boundary, what the team decided about APIs, and what is out of scope. That is why agents increasingly appear inside workspaces that link code, diagrams, and docs.
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.
When agents share context with humans — architecture sketches, ADRs on Notion, Slack decisions — they reduce rework. The agent's plan can be reviewed on the same board the team uses for planning.
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, and clear tasks.
- Code tasks require repository and architectural context
- Shared workspace memory improves agent plans and human review
- Autonomy demands guardrails — review, sandboxes, scoped tokens
How it works
A coding agent loop typically looks like: receive goal → retrieve relevant files and docs → propose plan → use tools (read, write, search, terminal, browser) → verify → report. Some loops pause for human approval before writes.
Context comes from embeddings over the repo, explicit file picks, issue trackers, and increasingly visual architecture maps. Connectors to GitHub (or local git) are table stakes; connectors to specs and diagrams reduce wrong assumptions.
Drawsy Agents focus on workspace-grounded tasks — expanding diagrams, analyzing options, summarizing decisions — rather than replacing your IDE. Pair an IDE agent for file edits with a workspace agent for planning and context-heavy questions.
- 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
Examples
Migration planning: an agent reads a service diagram on the canvas and a GitHub monorepo, then proposes an incremental extraction order — humans adjust on the board before any IDE agent cuts code.
Onboarding: a new engineer asks an agent how authentication flows work; retrieval pulls the auth ADR from Notion, the sequence sketch, and the relevant package in the repo.
Incident follow-up: linked Slack thread and architecture frame help an agent draft a postmortem outline with affected components — not a generic template.
Common approaches and problems
IDE-only agents excel at local edits but may ignore product context sitting in Notion or Figma. Workspace-only agents excel at planning but may not touch git. Many teams need both with a shared map.
Failure modes include infinite loops, silent partial fixes, and hallucinated APIs. Always review diffs; run CI; never grant production credentials to experimental agents.
Cost and rate limits matter. Long agent runs burn tokens and API quotas. Scope tasks narrowly and cache retrieved context where products allow it.
- IDE silo — great edits, weak product context
- Unreviewed autonomy — shipping risky changes
- Vague goals — agents wander or over-edit
- Missing diagrams — wrong service boundaries assumed
How Drawsy relates
Drawsy Agents operate on canvas plus connected context — GitHub, Notion, Slack, Drive, Gmail, Figma, PostgreSQL, HydraDB — so coding discussions stay tied to sources. They help research, structure, and analyze; they are not a replacement for your editor's agent or CI.
Connect a repo, sketch boundaries on the board at https://drawsy.tech/canvas, and run agents to expand structure or surface open questions before hands-on coding.
Agent runs vary by plan; there is no unlimited agent tier unless pricing explicitly says so. Drawsy does not claim agents always produce correct code. Use them to accelerate thinking and preparation, then verify in your toolchain.
Related concepts
Coding agents sit at the intersection of context engineering and connected context. See the blog posts on why agents need more than code and how agents understand a codebase.
