self-improving-agent

To make an AI agent learn from past interactions automatically, you use a self-improving agent with pre/post hooks to extract experiences from every skill run and update related patterns continuously.

Multi-memory architecture for lifelong learning agents uses semantic, episodic, and working memory to accumulate patterns and store episodic memories, translating them into actionable skill improvements.

To trigger self-correction in AI agents after a skill completes, you configure after_complete and on_error hooks that initiate automatic training cycles to capture experiences and update patterns.

Self-improvement works with code reviewers and debuggers by applying automatic training cycles across these agents after skill runs, capturing their experiences to guide future changes.

Evolution markers guide future AI agent improvements by tracking pattern updates, creating episodes, and measuring confidence during memory consolidation to direct subsequent skill refinements.

You should use hook-driven automation for agent memory management whenever you need to automate session logging and self-correction across multiple agents without manual updates.