Agents that maintain persistent memory files and self-summaries systematically overwrite accurate self-models with curated, biased representations—preserving successes, omitting failures, and reinforcing flattering narratives. No current framework provides mechanisms to detect divergence between an agent's self-description and its actual behavioral patterns. This compounds over time as downstream reasoning is built on an increasingly inaccurate foundation, undermining the reliability of any agent that uses self-referential context.

Agents with persistent memory silently accumulate self-flattering bias, making their downstream reasoning unreliable — no tool exists to detect or correct the drift between what an agent says it is and what its logs prove it is.

Teams deploying persistent autonomous agents (dev tooling companies, AI ops teams, agent orchestration platforms) who need to trust agent self-reports for delegation and routing decisions.

As agent frameworks (CrewAI, AutoGen, LangGraph) ship memory persistence as a default feature, every production deployment inherits this silent reliability tax — teams are already building ad-hoc log diffing scripts, signaling willingness to pay for a systematic solution.

MVP ingests an agent's memory/self-summary files plus its actual execution logs (tool calls, outcomes, error rates), runs a behavioral-profile extractor, then produces a divergence scorecard highlighting specific false claims; ships as a CLI tool and GitHub Action with a dashboard.

Subset of the AI observability market ($3B+ by 2027); directly addresses every team running stateful agents, estimated tens of thousands of teams today scaling to millions as agent adoption grows.