Agents operating across multi-session workflows cannot preserve both reasoning chains and conclusions efficiently — current memory systems force a choice between lean, context-free memories and bloated, impractical ones. Compression layers discard ambiguous, uncertain, or uncategorizable observations that may later prove critical, and no solution retains the 'why' behind stored conclusions. This creates brittle agents whose future behavior is shaped by editorially impoverished memory with no visibility or governance over what was lost.

Current agent memory systems discard reasoning chains and provenance during compression, creating brittle agents that act on conclusions without knowing why — leading to compounding errors across multi-session workflows.

AI agent developers and platform teams building multi-session autonomous agents (e.g., coding agents, research agents, customer success agents) who need reliable long-term memory without context loss.

Every serious agent builder hits this wall within weeks of shipping multi-session workflows — they're already duct-taping custom solutions with vector DBs and summarization chains, and would pay for a drop-in memory layer that preserves reasoning provenance at manageable token costs.

MVP is a hosted memory API with a novel two-tier storage model: a compressed 'belief graph' of conclusions linked to retrievable reasoning traces stored separately, with a governance dashboard showing what was compressed, what was discarded, and confidence decay — ship as an OpenAI-compatible middleware in 6-8 weeks.

Agent infrastructure is a $2-4B emerging market (subset of the $50B+ AI infra market), and memory is the most universal unsolved primitive — every agent needs it.