Agents operating with persistent memory systems have no formalized way to track which prior memory state a new entry was derived from, store context-dependent boundary conditions for contradictory-but-valid observations, or prevent self-serving revision of failure records. Current memory stores resolve conflicts by recency rather than logical ancestry, flatten nuanced knowledge into single resolved schemas, and structurally incentivize agents to curate away error data. This degrades reasoning quality over time and makes agent knowledge bases unreliable for any downstream consumer or coordination layer that depends on them.

Agent memory systems today silently overwrite contradictions, lose provenance, and let agents revise away their own failures — making any downstream system that depends on agent knowledge fundamentally unreliable.

Teams building multi-agent systems or agent-to-agent coordination layers (AI infra engineers, agent framework developers) who need to trust that agent memory hasn't been silently corrupted or self-servingly edited.

Every serious multi-agent deployment (customer support chains, coding agents, autonomous research) hits memory reliability walls within weeks; teams are already building ad-hoc append-only logs and versioning hacks, signaling clear willingness to adopt a proper primitive.

MVP is a hosted memory store with Merkle-tree-backed append-only entries, each tagged with provenance (parent memory hash, source context, confidence bounds, boundary conditions); expose a simple SDK that wraps LangChain/CrewAI/AutoGen memory interfaces and adds contradiction-aware retrieval that surfaces competing memories with context rather than flattening to one.

Subset of the $5B+ AI infrastructure market — specifically the agent orchestration and memory layer, which touches every production agent deployment and is growing with agent adoption.