Your README Is for Humans. Your AGENTS.md Is for Coding Agents

Why a Separate AGENTS.md Matters

Developers have long relied on README files to convey project purpose, setup instructions, and contribution guidelines to human readers. While this remains essential, modern development workflows increasingly involve AI‑powered coding agents that need a different kind of documentation. An AGENTS.md file bridges that gap by providing the agent with the context, constraints, and operational boundaries that humans intuitively understand from a README but that machines cannot infer from natural language alone.

Unlike a human, an agent lacks life experience, common sense, and the ability to ask clarifying questions. It operates on patterns it has seen during training and the explicit instructions you give it. By creating a dedicated AGENTS.md, you shift the responsibility of interpreting intent from the agent to a structured, machine‑readable format. This separation keeps your README clean for humans while ensuring the agent receives the precise guidance it needs to act effectively and safely.

What to Include in Your AGENTS.md

Start with a concise project overview that captures the agent’s domain: what the codebase does, its primary data flows, and any external dependencies it must interact with. This high‑level description helps the agent set expectations before diving into implementation details.

Next, define coding conventions. List preferred naming patterns, formatting standards, and any language‑specific idioms your team follows. Include a short note on testing philosophy, coverage expectations, and how the agent should handle edge cases. A quick reference to your linting, formatting, and security scanning tools prevents the agent from generating code that would later fail automated checks.

Provide a section titled “Allowed Commands and Tools.” Here you enumerate which CLI utilities, APIs, and SDKs the agent may invoke, as well as any rate limits or authentication requirements. If the project uses secrets management, describe the process for referencing environment variables or configuration files. Finally, outline boundaries and constraints: directories that are read‑only, files that must not be modified, and any regulatory or compliance rules that apply to the codebase. Clear limits protect both the project and the user from unintended side effects.

Writing for the Machine

Keep the language direct and imperative. Use bullet points for lists of commands or configuration snippets, and label each item with a short purpose tag. For example:

• Setup – Install dependencies via pip install -r requirements.txt
• Run Tests – Execute pytest --cov=src to verify changes
• Deploy – Use aws s3 sync dist/ bucket/ --exclude '*.tmp' for production releases

Avoid ambiguous phrasing such as “try to” or “if possible.” Replace with concrete instructions like “invoke build.sh before committing.” Include a “Troubleshooting” subsection that lists common agent errors and the corresponding corrective actions, enabling the agent to self‑correct without human intervention.

Remember to version your AGENTS.md alongside the rest of the repository. As the project evolves, update the file to reflect new tools, expanded scopes, or changed constraints. A living AGENTS.md ensures that each new generation of agents inherits the most current operational guidelines.

By treating AGENTS.md as a contract between your development team and the AI assistant, you gain consistency, safety, and productivity. The agent can focus on writing code, while you retain control over the overall direction and quality of the work.

Further reading: https://dev.to/johnnylemonny/your-readme-is-for-humans-your-agentsmd-is-for-coding-agents-16kg