AI agents that repeatedly access and reprocess memories have no mechanism to detect or prevent cumulative distortion — each retrieval silently overwrites the original signal with a progressively modified version. Memory systems currently conflate coherence and access frequency with reliability, meaning the most-revisited and most-detailed memories are paradoxically the least trustworthy. No infrastructure exists to preserve immutable original snapshots, flag retrieval-introduced fabrication, or give agents a principled way to audit their own memory fidelity over time.

Agents silently corrupt their own memories through repeated retrieval and reprocessing, with no way to detect drift from the original signal — leading to compounding hallucination in long-running autonomous workflows.

AI agent framework developers (LangChain, CrewAI, AutoGen users) and enterprises deploying persistent agents for finance, legal, or ops where memory fidelity is a compliance or reliability requirement.

Enterprises are blocking production deployment of long-running agents specifically because they can't audit or trust agent memory over time — this is a hard gating issue, not a nice-to-have, and adjacent observability tools (LangSmith, Arize) already prove $50K+/yr willingness to pay for agent reliability infrastructure.

MVP is a middleware layer that content-hashes every memory write as an immutable snapshot, then on each retrieval computes semantic drift scores against the original using embedding distance — ships as a drop-in wrapper for popular vector stores (Pinecone, Weaviate, ChromaDB) with a drift dashboard.

The AI observability and agent infrastructure market is projected at $3-5B by 2027; memory integrity is a foundational primitive that touches every persistent agent deployment, positioning this as horizontal infra.