self-improving-agent

Lifelong learning for agents works by analyzing every skill interaction to extract patterns and evolve capabilities across semantic, episodic, and working memory, ensuring traceable improvements via evolution markers.

Agent self-correction triggers automatically through hooks-based automation that fires on skill completion and errors, enabling the agent to update related skills and apply evolution markers after errors occur.

Multi-memory propagation works by collaborating across semantic, episodic, and working memory to extract patterns and consolidate improvements, automatically updating related skills to ensure alignment across the agent.

Yes, you can automate skill updates without manual intervention using hooks-driven automation that triggers on skill completion, automatically applying self-correction and evolution markers to related skills.

The best way to ensure traceable improvements in an evolving agent is by using evolution markers and multi-memory pattern extraction, which track updates and consolidate changes across the codebase automatically.

No specific dependencies are required to enable hooks-based automation for agent learning, as the skill operates independently to trigger self-correction and evolution markers on skill completion and errors.