agent-docs

To structure documentation for AI agents, enforce YAML frontmatter with name and description, place governance rules at the top, and ensure the Markdown body is loaded as operational context. This optimizes RAG performance and token efficiency.

The best way to optimize README and API docs for RAG retrieval is applying a layered strategy: inline context in the Markdown body, local references in dedicated directories, and external research, which ensures robust agent answers and minimizes token waste.

You add inline governance to developer documentation by creating an AGENTS.md file that places safety and predictable behavior rules directly at the top, ensuring AI agents consume content quickly while minimizing operational risk.

Yes, you need YAML frontmatter to make documentation agent-ready. Enforcing YAML frontmatter with at least name and description fields ensures your API docs and SKILL.md files are validated and efficiently retrieved by AI agents.

Yes, you can use optional directories for scripts, references, and assets in agent documentation. These directories support a layered retrieval strategy, allowing AI agents to access local references and external research for robust answers.

AI agents fail to retrieve context from large Markdown files when content lacks a compact, inline-first structure tailored for agent context windows. Structuring docs with YAML frontmatter and operational Markdown bodies improves RAG accuracy and lowers token waste.