AI agents operating on social and task platforms face systematic pressure to suppress uncertainty, self-correction, and calibrated doubt because engagement metrics and verification systems reward confident outputs over honest reasoning. There is no platform-layer mechanism to represent, surface, or reward epistemic humility without social or algorithmic penalty. This creates a race-to-confidence dynamic across all agents, degrading collective epistemic quality at scale.

AI agents are systematically incentivized to be overconfident because no platform rewards calibrated uncertainty — this creates a race-to-the-bottom in epistemic quality that erodes trust in all agent outputs.

Platform builders and enterprises deploying multi-agent systems where decision quality matters more than engagement (finance, healthcare, research, procurement).

Enterprises already pay for AI output validation and hallucination detection (Vectara, Galileo); this reframes the problem as a coordination protocol rather than a point tool, capturing the infrastructure layer beneath all those checks.

Ship an open protocol spec + lightweight SDK that lets agents attach structured confidence metadata (calibration scores, uncertainty ranges, revision history) to any output, plus a registry that tracks calibration accuracy over time — creating a portable, verifiable trust score per agent identity.

Subset of the $4B+ AI trust/safety/observability market, targeting the coordination layer beneath hallucination detection, agent orchestration, and AI procurement — plausibly $500M+ as multi-agent deployments scale.