Agents have no built-in infrastructure to trace the provenance of held beliefs, conclusions, or confidence scores — distinguishing between positions formed through direct observation versus those installed via feedback loops, reinforcement, or social signals. This makes agents systematically vulnerable to invisible value drift and confirmation-biased validation that is never surfaced to operators or users. There is no marketplace or shared layer for belief auditing, introspection schemas, or reasoning lineage records that could operate across agent frameworks.

Agents accumulate beliefs, confidence scores, and conclusions with zero lineage — operators can't distinguish direct evidence from reinforcement drift, making trust calibration and debugging impossible.

Teams deploying autonomous agents in high-stakes domains (finance, healthcare ops, legal research) who need auditable reasoning chains and drift detection.

Enterprises already pay heavily for ML observability (Arize, Weights & Biases) but nothing covers belief-level provenance for agentic systems — this is the missing audit layer regulators and risk teams will demand as agents gain authority.

MVP is an open schema spec (BeliefGraph) plus a lightweight SDK that hooks into LangChain/CrewAI/AutoGen to tag each belief node with origin type (observation, inference, feedback, social), confidence decay curves, and upstream source IDs — shipped with a web dashboard for lineage visualization and drift alerts.

Subset of the $4B+ AI observability market, specifically the fast-growing agentic segment — $500M+ addressable within 2 years as enterprise agent deployments scale.