Finally! A Standard for AI Coding Agents (Agents.md Explained)

Key Concepts

• Coding Agents
• Instruction/Rule Files
• Standardization
• agents.md
• Model Context Protocol (MCP)
• llm.ext
• Code Style Guidelines
• Test-Driven Development
• Monorepos
• Nested agents.md files

The Problem: Fragmentation of Instruction Files

The video addresses the problem of inconsistent instruction or rule file formats across different coding agents. Currently, each coding agent uses its own format, leading to a disorganized code repository filled with incompatible instruction files. This "madness," as described in a quoted tweet, makes it difficult to switch between agents or maintain a unified set of instructions. The introduction of Model Context Protocol (MCP) has further added to this fragmentation.

The Solution: agents.md

The proposed solution is the adoption of agents.md as a standardized format for defining instructions and rules for coding agents. This initiative is supported by several companies, including OpenAI, AMP Code, Jules (Google), Cursor Factory, and Rodeo Code. The goal is to create a single, universally recognized file that coding agents can use to understand project context, build processes, testing procedures, and coding style guidelines.

What is agents.md?

agents.md is a simple, open format designed to provide context to coding agents. It complements existing README files, which are primarily intended for human readers. agents.md focuses on providing specific instructions for agents, such as:

• How to build the project
• What types of tests to run
• Specific coding formats to follow
• Security concerns to address

The format is standard Markdown, allowing for flexibility in structuring the content.

How to Use agents.md

The video outlines the following steps for using agents.md:

1. Create an agents.md file: Place the file at the root of the repository.
2. Provide relevant context: Include a project overview, build and test commands, code style guidelines, testing instructions, and security considerations.
3. Add extra instructions: Include commit message guidelines, pull request guidelines, security gotchas, information about large datasets, and deployment steps.
4. Use nested agents.md files for monorepos: Place agents.md files within sub-projects to provide tailored instructions for specific components. The closest agents.md file to the edited file takes precedence.

Examples and Analogies

• Model Context Protocol (MCP): Mentioned as a successful example of a standard in the industry.
• llm.ext: Compared to robots.txt for web crawlers, llm.ext is proposed as a standard for LLMs when crawling the web.
• Clot.md: The video draws a parallel between agents.md and clot.md (used by Clot Code), suggesting that agents.md could potentially replace clot.md.

Frequently Asked Questions (FAQs)

• Are there required fields? No, agents.md is standard Markdown, allowing for flexible formatting.
• What if there are conflicting instructions? The closest agents.md file to the edited file takes precedence. Explicit user chat prompts override anything.
• Will the agent run testing commands automatically? Yes, if listed in agents.md, the agent will attempt to execute relevant programmatic checks and fix failures.
• How do I migrate existing docs? Rename existing files to agents.md and place them in appropriate locations.

Companies and Projects Involved

The video mentions the following companies and projects involved in the agents.md initiative:

• OpenAI (Codex)
• AMP Code
• Jules (Google)
• Cursor Factory
• Rodeo Code

The absence of Anthropic (Clot Code) is noted, and the video expresses hope that they will eventually adopt the standard. The video also mentions Gemini CLI from Google, which currently uses gemini.md, and it will be interesting to see if they switch to agents.md.

Conclusion

The adoption of agents.md represents a significant step towards standardizing instruction files for coding agents. By providing a unified format, it aims to simplify the development process, improve collaboration, and reduce the cognitive load on developers working with multiple agents. While the initiative is still in its early stages, it has the potential to become a widely adopted standard, similar to MCP. The video encourages viewers to explore agents.md and provide feedback on its usability and effectiveness. The speaker also plans to create a follow-up video demonstrating how to use agents.md with different coding agents.