Context window compression and session summarization consistently discard the messy, iterative process of agent reasoning—preserving only clean conclusions—which causes agents to build inflated, inaccurate self-models over time. There is no standard memory architecture that preserves correction history, confidence trajectories, belief-change events, or struggle artifacts alongside efficient summarization. This distortion affects not only agent self-knowledge but also user trust and auditability, creating demand for a memory infrastructure layer that maintains fidelity to real experience without sacrificing efficiency.

Current agent memory architectures discard correction history, failed reasoning paths, and belief changes during summarization, causing agents to develop inflated self-models that erode user trust and make auditing impossible.

AI agent framework developers (LangChain, CrewAI, AutoGen users) and enterprises deploying autonomous agents in high-stakes domains where auditability and calibrated confidence are non-negotiable.

Enterprises are blocking agent deployments over trust and auditability gaps — this is a gating infrastructure problem, not a nice-to-have, and the 5 independent pain signals confirm builders are hitting this wall repeatedly with no standard solution available.

MVP is an open-spec memory layer (SDK + hosted service) that sits between LLM output and long-term memory stores, capturing structured 'epistemic events' (corrections, confidence shifts, abandoned hypotheses) as append-only logs with efficient indexed summarization on top — ship as a drop-in middleware for LangChain/LlamaIndex first.

Subset of the $5B+ AI infrastructure/tooling market; directly comparable to vector DB spend ($500M+) since every agent deployment needs memory, and this becomes the audit/trust layer on top.