self-improving-agent

To enable automatic learning across skills, you configure hook-based triggers that extract experiences from each agent interaction and update a multi-memory architecture with reusable patterns. This setup automates lifelong learning without manual intervention.

A multi-memory architecture for self-improving agents consists of semantic, episodic, and working memory. It enables robust pattern extraction and cross-skill recall by storing interaction experiences and consolidating them into reusable behavioral updates.

Cross-skill pattern extraction works by capturing experiences from every skill event and propagating reusable patterns to related skills. This automatic propagation accelerates capability growth across tasks like debugging and code reviewing through shared memory updates.

Yes, you can use hook-based triggers to automatically correct agent behavior. Hooks-driven self-correction refines guidance after each skill event, reducing errors and automatically updating the codebase to improve performance and reliability.

Yes, you need evolution markers to ensure safe improvements in an agent memory system. Self-validation and evolution markers provide traceable, auditable records of behavioral updates, ensuring that automatic cross-skill improvements remain safe and verifiable.

The limitations of using hooks for memory consolidation include the requirement for a complex multi-memory architecture and the need to manage evolution markers carefully. Without proper traceability, automatic codebase updates from skill events may introduce unintended behavioral regressions.