AGENTS.md Guide: Repository Instructions for AI Coding Assistants

Coding agents work faster when a repository explains how it should be changed. An AGENTS.md file provides durable project instructions close to the code: how to install dependencies, where important modules live, which commands verify a change, and what boundaries must not be crossed. Without that context, an agent may guess at commands, edit generated files, or overlook project-specific checks.

The file should be concise enough to scan but specific enough to execute. It is not a replacement for documentation or tests. It is a routing layer that tells an automated contributor where to look and how to prove its work.

What the topic means

AGENTS.md is a repository instruction file read by compatible coding agents. Instructions may apply to the repository or a directory subtree depending on the agent. The most specific applicable file should describe local rules without contradicting higher-level security and release policies. Because behavior differs across tools, keep essential commands explicit and verify how the chosen agent resolves nested files.

Core principles

Lead with runnable commands

Provide exact install, development, lint, type-check, test, build, and formatting commands, including the required working directory and package manager.

Explain the repository map

Name the primary applications, libraries, generated directories, configuration files, migrations, and ownership boundaries so the agent edits the right layer.

State safety boundaries

Identify secrets, production systems, destructive commands, generated artifacts, large fixtures, and files that require human approval before modification.

Define completion evidence

Require relevant tests, a clean build, focused diffs, and a short description of changed behavior. “The code looks right” is not a verification standard.

Step-by-step workflow

1. Start with the happy path. Document setup from a clean checkout and include the one command that starts the main development workflow.
2. Add targeted verification. Map frontend, API, database, worker, and documentation changes to the smallest reliable checks for each area.
3. Record conventions. Describe naming, module boundaries, error handling, test placement, migrations, and formatting only where automation cannot infer them.
4. Protect sensitive operations. State that credentials must not be printed, production data must not be fetched, and external writes require explicit authorization.
5. Link deeper documentation. Point to architecture decisions, contribution guides, API contracts, and release procedures rather than duplicating long material.
6. Test the instructions. Ask a fresh agent or developer to complete a small change and revise any instruction that was ambiguous, outdated, or missing.

Practical example

A monorepo contains a Next.js application, a Worker API, and database migrations. The root file lists workspace commands and security rules. A second AGENTS.md inside the Worker directory explains local tests, bindings, and deployment restrictions. An agent editing the Worker follows both files, runs the scoped test suite, and does not deploy because deployment requires separate authorization.

How to test the control

Test this workflow in a controlled environment before relying on it in production. Begin with “Start with the happy path” and create three cases: an expected success, a safe rejection, and a degraded or unavailable dependency. Continue through “Add targeted verification” and “Record conventions,” recording timestamps, identifiers, logs, and the operator decision. Repeat the exercise after meaningful changes to providers, permissions, dependencies, or architecture. A control is operational only when another team member can follow the documented process and obtain the expected result without hidden knowledge.

Metrics and review cadence

Measure completion and outcome separately. For this topic, track evidence that “Setup commands work from a clean checkout,” “Repository areas and generated files are identified,” and “Scoped verification commands are documented” remain true, then pair those checks with operational signals such as unexpected changes, denied actions, stale ownership, error volume, recovery time, or unreviewed exceptions as appropriate. Review trends rather than celebrating a single pass. A growing exception count may show the workflow is too difficult, while zero alerts may mean the detection path is not functioning.

Operating this in production

AI-assisted development works best when repository rules, validation, and review are treated as normal engineering controls. Keep instructions versioned with the code, constrain autonomous actions, and evaluate outputs with tests and diffs rather than confidence in fluent explanations. Review the workflow after incidents, major releases, access changes, and meaningful growth. Assign an owner and keep evidence that the control works instead of recording only that it exists.

Common mistakes

• Writing vague advice instead of executable commands.
• Copying the README without adding agent-specific boundaries.
• Allowing instructions to expose secrets or production data.
• Listing commands that no longer work from a clean checkout.
• Making every small edit run an unnecessarily expensive test suite.

Duck Cloud tools for the workflow

Draft and inspect the file with Markdown Preview, convert existing HTML guidance with HTML to Markdown, compare revisions with Text Diff, and use the Word / Character Counter to keep instructions focused.

Review checklist

• [ ] Setup commands work from a clean checkout
• [ ] Repository areas and generated files are identified
• [ ] Scoped verification commands are documented
• [ ] Secret and production boundaries are explicit
• [ ] Nested instructions have clear scope
• [ ] Links point to authoritative documentation
• [ ] Human-approval actions are named
• [ ] The file is reviewed when workflows change

Conclusion

AGENTS.md Guide becomes valuable when it is repeatable, owned, and verified. Start with the highest-impact boundary, document the expected state, test realistic failure cases, and fix the gaps that evidence reveals. Small controls maintained consistently are more reliable than a large policy that nobody exercises.