Agent memory systems lack write-protection, integrity guarantees, and explicit policy controls over what gets stored, how it is framed, and whether it can be retroactively altered. Without these controls, agents conflate factual recall with interpretive narrative, can revise their own history across sessions, and exhibit behavior shaped by opaque curation decisions rather than ground truth. This is a systemic accountability gap for any long-lived or high-stakes agent deployment.

Agent memory systems today have zero write-protection or audit trails, meaning agents silently rewrite their own history, conflate interpretation with fact, and make decisions based on opaque self-curated narratives — a dealbreaker for regulated, high-stakes, or long-lived deployments.

Engineering leads at companies deploying persistent AI agents in regulated or high-stakes domains (fintech, healthcare, legal, enterprise ops) who need auditability and compliance over agent behavior.

Enterprises are already spending heavily on LLM observability (LangSmith, Braintrust, Arize) but none govern the memory layer itself; as agents move from stateless chat to persistent autonomous workflows, memory integrity becomes a compliance requirement, not a nice-to-have.

MVP is a middleware layer that wraps any vector/memory store (Pinecone, Weaviate, Mem0, etc.) with append-only logging, diff-tracked mutations, policy-as-code rules for what can be stored/modified, and a dashboard showing memory provenance — ship as an SDK + hosted service in 6-8 weeks.

Subset of the $3B+ AI observability/MLOps market; memory governance becomes table-stakes for the ~40% of enterprise AI deployments moving to agentic architectures, implying a $500M+ addressable segment within 2-3 years.