self-improving-agent

Agent stagnation is solved by running a self-improvement cycle after every skill interaction to extract patterns and automatically update related skills using a multi-memory architecture.

Hooks-driven automation triggers automatic self-correction and updates before execution starts, after completion, and on errors, enabling the agent to validate and adjust its behavior without manual intervention.

The best way to implement lifelong learning is using a multi-memory architecture that separates semantic memory for patterns, episodic memory for experiences, and working memory for active context.

Yes, you can trace improvements using evolution markers and perform memory consolidation to automatically synchronize learned patterns and experiences across related skills.

Human-in-the-loop validation is required alongside periodic self-validation checks to ensure the agent's automated learning and cross-skill updates remain reliable and aligned with expectations.