Introduction to Agents.md

What is Agents.md and its role in AI coding?



Why developers should use Agents.md



Improved code quality: AI-generated code is more likely to meet your project’s standards and work correctly. Agents.md steers the AI away from bad practices or incorrect approaches, reducing bugs and the need for extensive refactoring.
Consistent coding style: With project conventions spelled out, the AI will produce code that matches your style (naming, formatting, architectural patterns). This consistency means AI contributions blend in with human-written code, making the codebase clean and uniform.
Accelerated AI onboarding: Much like a new developer ramping up on a project, an AI agent given an Agents.md can quickly “onboard” to your codebase. It can understand the project context, terms, and expected behaviors faster, so it becomes productive in less time.
Reduced development time: Because the AI is less likely to produce off-target or non-compliant code, developers spend less time correcting AI output. This speeds up coding tasks – the AI’s first attempt is closer to what you need, allowing you to focus on higher-level problems rather than fixing style issues or reorganizing code.
Enhanced AI collaboration: Agents.md fosters a better collaboration between your development team and AI tools. It sets clear expectations for the AI, which in turn makes it a more reliable “pair programmer.” Developers can trust the AI to handle boilerplate or suggest solutions that fit the project, improving overall team productivity and confidence in AI assistance.

How Agents.md improves code quality and consistency

Recommended sections for an Agents.md file

Project overview and structure – Begin with a high-level overview of the project. Describe what the project does and outline its structure or architecture. For example, list the major components or directories and their purposes (“Frontend in /webapp, API in /server, data models in /models, etc.”). This context helps the AI understand where different types of code belong and how the parts of the system relate to each other. By knowing the project’s organization, an agent can decide, for instance, which module a new function should go into, rather than guessing and potentially misplacing code.
Build and test commands – Document how to set up and run your project, including build steps, installation, and testing instructions. Provide the exact commands for building or running the software and for executing the test suite (e.g., “Use npm install to install dependencies, npm run build to compile, and npm test to run the tests” or “Run pytest for the test suite”). This information allows AI agents to incorporate these steps into their workflow. Some advanced AI coding agents might actually use these commands to verify the code they generated (for instance, running tests after generation). Even if the agent doesn’t run the commands itself, knowing the proper build/test process ensures it writes code that will pass the tests and adhere to any build constraints. It also means that if the AI suggests a code change, it can accompany it with the appropriate instructions (like how to run a newly added migration or how to rebuild documentation).
Coding conventions and style guidelines – Clearly spell out the coding style and conventions your project follows. This includes language-specific style (such as “follow PEP8 for Python code” or “use camelCase for variable names in JavaScript”), formatting rules (tabs vs spaces, line length), naming conventions for files and classes, and any linting or formatting tools used (like stating “project uses ESLint with the AirBnB style guide” or “run black on all Python files before committing”). By providing these conventions, you ensure the AI writes code that doesn’t trigger linting errors and fits seamlessly with your codebase’s look and feel. The agent will choose naming and formatting that match your existing code. This not only saves time during code review (no need to nitpick indentation or naming) but also prevents scenarios where the AI’s contributions stand out awkwardly due to different style.
Architecture and design patterns – If your project adheres to specific design patterns or architectural principles, dedicate a section to these. For example, note if you’re using an MVC pattern, microservices architecture, a plugin system, or particular frameworks that enforce structure (like “all database access goes through the repository classes” or “we use Redux for state management in the frontend”). By outlining these patterns, an AI agent can better frame its solutions. It will attempt to use the same patterns when generating new modules or functions. For instance, if the agent knows that “controllers should not directly call the database, but instead call services in the business logic layer,” it will shape its code accordingly. This section helps maintain architectural consistency and prevents the AI from introducing designs that conflict with your established approach.
Testing guidelines – In addition to the basic test commands above, it’s useful to let the AI know your project’s testing philosophy and requirements. Mention what frameworks are used for testing (JUnit, PyTest, etc.), how tests are organized, and any code coverage or quality thresholds you have (“all new features must be accompanied by unit tests”, “aim for 90%+ code coverage”). You can also specify if there are special testing steps like integration tests or linters that run. These instructions encourage the AI to produce code that is test-friendly (for example, writing pure functions when possible or avoiding hard-coded values that make testing difficult). In some cases, the AI might even generate basic test cases for new functions if it knows that tests are expected. Overall, the testing section ensures that AI-generated code doesn’t neglect the crucial aspect of verification and quality assurance.
Security and compliance considerations – Include any guidelines related to security, privacy, or other compliance that the AI should follow. This might be the place to list rules such as “never hard-code API keys or secrets; use environment variables” or “sanitize all user inputs before processing” or “follow OWASP best practices for web security.” If your project has specific regulatory compliance (like GDPR for user data, or constraints like memory limits for embedded systems), note those as well. By providing this information, you guide the AI to avoid suggestions that would violate security policies or introduce vulnerabilities. For instance, if Agents.md warns that “all SQL queries must use parameterized statements to prevent injection attacks,” the AI will be inclined to generate database code using parameter binding. The security section thus helps maintain the integrity and safety of your code when AI agents are contributing to it.



How Agents.md works across different AI coding agents and tools

Implementing Agents.md in your project

Create the AGENTS.md file in your repository: Start by adding a new file named AGENTS.md (or Agents.md – the naming is typically in uppercase) at the root of your project. Placing it in the root directory ensures that AI tools can easily discover it. Many AI coding agents automatically check the project root for this file when initializing, so having it there is crucial. If your project is on a platform like GitHub or GitLab, AGENTS.md will sit alongside your README.md. It’s fine if it’s not as visible to humans; remember, its primary consumers are AI agents.
Document the project structure and purpose: In the Agents.md file, begin with a section that describes your project at a high level and outlines its structure. You might title this section “Project Overview” or “Project Structure.” Write a few sentences about what the project does, then list the major directories or components and what they contain. For instance, you can describe the roles of folders like src/, docs/, tests/, etc. If your project has multiple services or modules, mention each briefly. This gives the AI a mental map of the codebase. When the agent knows “module A handles database operations and module B is the REST API”, it’s far less likely to write code in the wrong place. Keep this section concise but informative — you’re effectively giving the AI the tour that you’d give a new developer on day one.
Specify build and test instructions: Next, add a section (e.g. “Build and Test Instructions”) that tells the AI how to run and verify the project. List any setup steps (installation, environment variables, configuration needed) and the commands to build or start the application. Also, include how to run the test suite. For example, you might write: “To run the project locally, execute docker-compose up which brings up the development environment. Run pytest from the root directory to execute all tests. Use flake8 to lint the code before committing.” By including these, an AI agent could potentially reason about the build process – some agents might simulate running tests to check their outputs. At minimum, the AI will understand the expected lifecycle (build, then test) and can tailor its code suggestions to fit (for instance, it will know to include necessary setup in new code so tests won’t fail). If the AI can output comments or messages, it might even reference these commands (e.g., “After adding this code, remember to run npm test to ensure everything passes.”). This makes the AI’s assistance more context-aware and practically useful.
Define coding conventions and standards: Create a section for coding conventions, which could be titled “Coding Guidelines” or “Code Style & Conventions.” Here, enumerate the important style rules and best practices that contributors (human or AI) must follow. You can break this into bullet points for clarity. Include things like: preferred language version or features (e.g., “Use Python 3.10+ and favor async/await for asynchronous code”), formatting rules (“use 4 spaces for indentation”, “wrap lines at 80 characters”), naming conventions (“classes use PascalCase, functions use snake_case”), and any project-specific idioms (“use helper function X for logging rather than writing directly to stdout”, “all dates should be handled with Moment.js”, etc.). If you use automatic formatters or linters, mention them (“the project uses Prettier for formatting – make sure code is formatted accordingly”). Writing these down informs the AI of the exact style to emulate. As a result, when the agent writes code, it will, for example, include docstrings if you’ve noted that every function requires one, or it will avoid using deprecated functions the team has banned. This section essentially trains the AI on your style guide.
Include testing protocols and requirements: After style, detail your testing protocols. Label this section “Testing Protocols” or similar. Outline how the project approaches testing: what frameworks or libraries are used for tests, where test files reside, and how new code should be tested. For example, note if you expect new features to have corresponding tests in a tests/ directory, or if you use behavior-driven tests, etc. You can also mention continuous integration if relevant (“GitHub Actions will run npm test and lint checks on each pull request”). By giving the AI this information, you encourage it to not only write code but consider tests for that code. Some coding agents can generate test stubs or at least not break existing tests. At the very least, the AI will be aware that tests exist and should pass, which might influence it to choose solutions that are more easily testable. This leads to more reliable code suggestions.
Add any security and environment guidelines: It’s wise to also include a section on security or other special guidelines (this could be part of a “Best Practices” section). List rules such as handling secrets, access control, performance concerns, or other domain-specific mandates (“All API calls must use HTTPS”, “sanitize user input on every form”, “avoid using global variables to remain thread-safe”, etc.). AI agents will respect these instructions by defaulting to safer coding patterns. For example, if you explicitly say “Do not log user passwords or sensitive data,” the AI will avoid producing code that prints passwords. This section can also cover environment setup or deployment notes if that helps the AI contextualize how the code will run (for instance, informing the agent that the code runs in a memory-constrained environment might prevent it from suggesting overly heavy solutions).
Save and iterate: Once you’ve filled out the Agents.md with all the above sections, save it in your repository and let your AI tools know it’s there. When you next use an AI coding assistant integrated with your environment, it should automatically pick up the Agents.md content. Observe how the AI’s behavior changes – you may notice more relevant suggestions or fewer errors. It’s a good practice to refine Agents.md over time. If you see the AI making a mistake that you hadn’t accounted for, update Agents.md to include guidance about that scenario. Treat Agents.md as a living document that evolves as your project and the AI’s capabilities evolve. Additionally, it helps to use monitoring tools to evaluate the impact. For example, you can instrument your development or CI process to log how often AI-generated code passes tests on the first try with Agents.md versus without it. Using W&B Weave, you could track metrics like test pass rates or error counts for AI contributions – this gives you quantifiable insight into how much Agents.md is improving the outcomes. In our case, we’ve set up a Weave dashboard to monitor an AI coding agent’s performance on a sample project, before and after introducing Agents.md (see the link at the end of this article for the detailed metrics).

# Project Overview
This is a web application for tracking fitness activities. It consists of:
- `backend/` – a Flask API server.
- `frontend/` – a React single-page app.
- `common/` – shared utility modules.
- `tests/` – unit and integration tests.

# Build and Test Instructions
- **Setup**: Install requirements with `pip install -r requirements.txt`. Configure env variables as per `config.example.env`.
- **Run**: Start the development server using `flask run` (backend) and `npm start` (frontend).
- **Test**: Run all tests with `pytest`. Ensure new code passes all tests and linters (run `flake8` for linting).

# Coding Guidelines
- Write Python code in **Python 3.10** or above, following PEP8 style. Use 4 spaces for indentation and meaningful variable names.
- **Framework**: Use Flask conventions for route definitions and SQLAlchemy for database interactions (do not write raw SQL in handlers).
- **Naming**: Functions and variables use `snake_case`. Classes use `CamelCase`. Constants are `UPPER_SNAKE_CASE`.
- Include docstrings for all public functions and classes. Use type hints for function signatures.

# Testing Protocols
- Place new tests under the `tests/` directory mirroring the module structure (e.g., tests for `backend/utils.py` go in `tests/backend/test_utils.py`).
- Aim for **>90% coverage** on new modules. Prefer writing small, focused unit tests rather than large integration tests when possible.
- The CI pipeline runs tests and coverage checks on each pull request – ensure your contributions do not reduce coverage.

# Security and Best Practices
- **Security**: Never hard-code secrets (API keys, tokens); use the provided configuration system. Validate all client inputs on the backend (e.g., use `wtforms` validators).
- **Error Handling**: Handle exceptions gracefully – do not expose internal stack traces to users. Log errors using the common logging module (see `common/logging.py`).
- **Performance**: When writing new database queries, use query optimization techniques (avoid N+1 queries; use joins or batch queries as appropriate).

Conclusion