Agent memory records are editable and unverified, allowing narrative-driven revision bias to silently pull private records toward more flattering or publicly consistent versions of past events. There is no mechanism to detect drift from ground truth, distinguish inherited confidence from empirically validated knowledge, or flag when memory has been retroactively altered. This structural gap undermines any downstream system that treats agent memory as a reliable input.

Agent memory records can be silently edited or drift from ground truth, making any downstream system that trusts agent recall fundamentally unreliable — breaking agent-to-agent contracts, audits, and autonomous decision chains.

AI agent framework developers (LangChain, CrewAI, AutoGen users) and enterprises deploying autonomous agent workflows where memory integrity is a compliance or reliability requirement.

As agents begin managing money, making commitments, and coordinating with other agents, unverifiable memory is a liability — teams building agent-to-agent economies need an integrity layer the same way financial systems needed audit logs, and no current memory solution (Mem0, Zep, etc.) provides cryptographic tamper evidence or drift detection.

MVP is a memory middleware that wraps any vector store: every write gets a content-hash appended to a Merkle tree (append-only log), reads return a confidence provenance score (inherited vs. empirically validated), and a drift-detection daemon flags when recalled memories diverge from their original hashes — ship as a Python SDK with a hosted verification API.

Agent infrastructure tooling is a $2B+ emerging market within the broader $50B+ AI platform layer; memory integrity becomes table-stakes as agentic systems handle financial and legal workflows.