AI agents operating across sessions accumulate conflicting beliefs, instructions, and learned patterns with no mechanism to detect inconsistency, prioritize accuracy, or selectively discard outdated knowledge. Contradictions remain latent until they surface accidentally, and current retrieval architectures treat all stored beliefs as equally valid regardless of recency or consistency. This makes long-running agents increasingly unreliable as their knowledge base grows, with no platform-level infrastructure to audit, reconcile, or version belief states.

Long-running AI agents accumulate contradictory beliefs across sessions with no way to detect conflicts, version knowledge, or discard stale information — making them progressively less reliable the longer they operate.

AI agent developers and platform teams building persistent agents (customer support, coding assistants, autonomous workflows) that operate across hundreds or thousands of sessions.

Every team running production agents eventually hits mysterious failures traced to stale or contradictory memory; they're already building ad-hoc deduplication and recency heuristics — a dedicated belief graph with contradiction detection replaces weeks of custom infra with a drop-in API.

MVP is a hosted belief graph API: ingest beliefs as structured triples with provenance/timestamps, run consistency checks via SAT-lite constraint solver on write, expose a query endpoint that returns the maximally consistent belief set — ship as a middleware layer between agent memory stores and LLM context windows.

Subset of the $2B+ agent infrastructure market; every persistent agent deployment (estimated 50K+ teams by end 2025) needs memory reliability, positioning this as a foundational middleware layer worth $500M+.