auto-improve

To audit agent memory for staleness and contradictions, you run an evidence-driven eval loop that scores recent outputs, detects silent memory degradation, and rewrites or consolidates inconsistent memory entries while generating an audit report.

Preventing documentation drift in iterative agent workflows requires running an evidence-driven improvement loop that detects file changes, evaluates repeated friction, and applies targeted mutations to documentation based on structured severity and frequency rankings.

A conversation review loop captures durable user preferences by triggering a background review every 10 conversation turns, extracting genuinely durable insights, and saving them to memory without interrupting the primary task execution.

You measure AI agent performance improvements over multiple runs by executing a structured binary eval suite that generates dashboards and tracks results in JSON or TSV formats to quantify output scoring across iterative execution cycles.

Yes, you can run a memory audit using safe non-request-routed activation logic, which allows background conversation review and memory repair to execute without disrupting the primary request routing or main task workflow.

You should apply an eval loop to fix weak or inconsistent agent outputs when repeated execution failures, silent memory degradation, or documentation drift account for a recurring portion of degraded performance across iterative sessions.