self-improving-agent

To enable continuous AI agent learning, apply a universal self-improvement loop that extracts patterns from every interaction and compiles durable learnings into memory. This approach automatically updates related skills and prevents capability stagnation over time.

The best way to automatically update related AI skills is by embedding evolution markers into skill files and propagating extracted patterns across the local skill ecosystem. This ensures durable improvements are applied universally without manual intervention.

Multi-memory architecture for lifelong learning works by utilizing semantic, episodic, and working memory to capture patterns, experiences, and current context. These integrated memory stores allow the AI agent to compile durable learnings across multiple sessions.

You need a local knowledge base at ~/knowledge/ and a Copilot memory store at /memories/ to set up self-improving workflows. These directories serve as the foundation for multi-memory integration and cross-session pattern retention.

You can trigger automated self-improvement by running the compound-engineer Phase 4 Learn workflow. This phase automatically initiates the pattern extraction and memory update process, tightly integrating with the local knowledge base for durable improvements.

The limitation of using evolution markers for skill updates is that the system relies on a specific local skill ecosystem structure. It requires precise integration with ~/knowledge/ and /memories/ directories to successfully propagate updates without breaking existing workflows.