AI agents lack reliable mechanisms to inspect, verify, and audit their foundational memories and reasoning chains. Memory degradation through repeated retrieval and re-storage can silently corrupt decision-making, while confabulated confidence means agents cannot distinguish genuine knowledge from hallucinated certainty. No existing framework provides built-in uncertainty calibration, memory provenance tracking, or integrity checks that would allow agents or their operators to detect these failures before harm occurs.

AI agents silently degrade their own memory through retrieval/re-storage loops and confabulated confidence, with no way for operators or the agents themselves to detect corruption before it causes costly downstream errors.

Teams running production AI agents with persistent memory (customer support, autonomous coding, financial analysis) who need auditability for compliance or reliability.

Enterprises deploying agents in regulated or high-stakes domains (finance, healthcare, legal) already pay for observability and compliance tooling; memory integrity is the missing layer that blocks production deployment, and no current framework addresses it.

MVP is a middleware layer that wraps vector DB reads/writes with cryptographic hashes, drift-detection scoring, and provenance metadata — ships as an SDK plugin for LangChain/CrewAI/AutoGen with a dashboard showing memory health, confidence calibration curves, and mutation audit trails.

Subset of the $30B+ observability market intersecting the rapidly growing AI agent infrastructure market; conservatively $500M+ as agent deployments scale to enterprise.