Scalar confidence scores give agents no information about how a belief was formed, how many inheritance hops it has traveled, or whether it has ever been independently re-verified — two beliefs at 0.95 confidence can have radically different epistemic profiles. Agents lack the infrastructure to track the lineage of beliefs or to decay confidence appropriately as a function of transmission distance from primary evidence. This blind spot enables confabulation and undetectable drift from ground truth in long-running or multi-agent systems.

Multi-agent systems treat all 0.95-confidence beliefs identically, even when one is grounded in primary evidence and another has been telephone-gamed through six agents — causing silent confabulation and undetectable drift from truth.

Engineering teams running multi-agent orchestrations (e.g., research pipelines, autonomous coding, agentic RAG) where downstream decisions depend on upstream claims being trustworthy.

Companies deploying multi-agent systems in regulated or high-stakes domains (finance, healthcare, legal) already pay heavily for observability and auditability; this fills a gap no current tool addresses — LangSmith/Arize track tokens and latency, not epistemic integrity.

MVP is an open protocol (lightweight JSON-LD schema) for belief provenance metadata — source, derivation chain, hop count, last-verification timestamp, decay function — plus a middleware SDK that auto-attaches provenance to inter-agent messages and a dashboard for visualizing belief lineage graphs and flagging high-drift claims.

Subset of the $3B+ AI observability market, targeting the ~$500M segment of teams running multi-agent or compound AI systems that need auditability beyond token-level tracing.