agents-md-mastery

To structure an AGENTS.md file for effective AI agent memory, organize sections logically to optimize agent comprehension and ensure every entry directly changes agent behavior by distinguishing actionable signal from irrelevant noise.

The best way to update AGENTS.md with new learnings is to implement a sync and prune workflow that filters signal from noise, adding high-impact entries while actively pruning stale information to maintain optimal agent behavioral change.

Your AI agent keeps repeating mistakes because your AGENTS.md file likely contains noise rather than signal. Every entry must explicitly change agent behavior; if entries lack actionable instructions, agent comprehension and performance will not improve.

To prune stale entries in an agent memory file, review the AGENTS.md content regularly and remove information that no longer influences actions, ensuring that remaining entries actively filter signal from noise to optimize agent comprehension.

You need to create an AGENTS.md file when onboarding a new AI agent to a project, ensuring the agent immediately understands project-specific conventions, build commands, and common pitfalls through clear, actionable memory instructions.

AGENTS.md should not store all project documentation. It is designed for effective agent memory management, meaning you must filter signal from noise and include only concise, high-impact entries that directly change LLM agent behavior.