consensus-loop:retrospect

You can extract learnings from session audit history by mining the current session data and translating it into durable memories. The process deduplicates and organizes the extracted information for quick retrieval during future retrospectives.

Retrospective memory maintenance should be run after completing a track, during a retrospective, or at the end of a session. This ensures that session learnings are captured and persisted while the context is still fresh.

Memory governance prevents memory pollution by enforcing access control and integrity checks on extracted learnings. This ensures that only deduplicated, verified memories are stored for future reference.

The best way to deduplicate session learnings is to use a retrospective memory maintenance process that automatically mines session data, removes duplicates, and organizes the remaining learnings into durable memories.

Yes, you can mine audit history and conversations to generate structured memories automatically. The extracted learnings are then deduplicated and organized for quick retrieval.

No external dependencies are required to perform memory governance on session learnings. The process operates independently to extract, deduplicate, and store memories with access control and integrity checks.