self-improving-agent

Agent interactions are captured using hook-driven session logging, which records skill start, completion, and error events to extract reusable patterns for future runs. These logged sessions enable multi-memory tracking across semantic, episodic, and working memory.

Skill self-improvement is the process of capturing what happens during skill interactions, extracting reusable patterns, and feeding validated corrections back into guidance to prevent repeated mistakes and eliminate stale instructions across planning, debugging, and implementation tasks.

Debugging patterns discovered in one session are promoted into the relevant skill guidance through correction markers and multi-memory tracking, allowing the extracted lessons to automatically inform future planning, review, and implementation tasks across skills.

Hook-driven session logging automates error recovery by running on skill start, completion, and error events, recording errors, revising guidance, and validating whether updated instructions maintain traceable and accurate skill evolution.

Multi-memory tracking uses correction markers to maintain traceable, validated skill updates across semantic, episodic, and working memory, ensuring that extracted patterns and revised guidance remain accurate and reusable for future agent runs.

Self-improving agent skills require hook-driven session logging, multi-memory tracking, and correction markers to function; without these mechanisms, traceable skill evolution and cross-skill guidance updates cannot be validated or maintained.