continuous-learning

To save debugging insights for cross-session reuse, you need to capture trajectory logs and extract discoveries into structured skill templates. This process converts transient debugging findings into persistent knowledge for future activation.

Continuous learning for agent debugging is the process of documenting hard-won discoveries into an evolving skills library. It uses memory augmentation to validate and store new skills, preventing repeated debugging efforts across sprint lifecycles.

You extract knowledge from trajectory logs by reviewing the documented debugging discoveries and converting them into structured skill templates. These templates are then written to memory zones for approval and archival as reusable skills.

Yes, you need access to Grimoires memory zones to document, validate, and store new agent skills. The system writes pending skills extracted from debugging sessions to this memory state for later review and activation.

To activate pending skills during an active Loa session, you trigger the review process to evaluate extracted skill templates. Once approved, the skills move from pending to Active, allowing future sessions to leverage the reusable patterns.