AI Memory System Uses Code-Review-Style Pipeline to Keep Knowledge Bases Clean

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A developer framework called ai-assistant-dot-files treats AI memory not as simple note-taking but as a structured promotion lifecycle, moving information through stages: Capture, Candidate, Audit, Approve, Index, Retrieve, and Expire. Rather than saving every useful insight immediately, new learnings first become Candidate Records with required metadata fields, then undergo a human-led audit before being approved for durable storage. The design intentionally mirrors code review, since persistent memory directly influences future agent behavior and therefore warrants a traceable approval trail. Explicit rejection rules mean that promoting zero candidates in a cycle is considered a healthy outcome, not a failure. Expired knowledge items are archived rather than deleted, preserving a record of what the team once believed and why it was eventually superseded.

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