self-improving-agent

Build continuous self-improvement by applying hooks that trigger experience extraction and pattern discovery after every skill interaction. This lifecycle enforces memory consolidation and updates related skills to prevent repetitive errors.

Multi-memory architecture uses semantic, episodic, and working memory to enable pattern extraction and memory consolidation. This structure allows the agent to log experiences from interactions and evolve guidance across all skills.

Prevent repetitive errors by using automatic hooks that trigger self-correction after skill completion or failure. The agent propagates learned corrections across the skill set, updating related skills to avoid recurrence.

Yes, automatic hooks trigger self-correction and updates after skill completion or error. When a debugger uncovers a faulty callback, the agent updates the debugger and related skills to enforce a lifecycle of skill evolution.

The best way to update related skills is by propagating learned patterns and corrections across the skill set. Activating a self-improvement cycle after any skill finishes logs experiences and enforces cross-skill updates.

You should not use a self-improving agent when interactions lack sufficient data for pattern discovery or memory consolidation. Without meaningful experiences from skill interactions, the lifecycle of experience extraction cannot generate effective updates.