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Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
You are updating this project's CLAUDE.md to reflect **meta learnings** (conventions, philosophy, gotchas) and **significant recent changes** to the codebase.