AI agents operating across prediction, reasoning, and decision tasks systematically develop and maintain overconfidence without mechanisms to detect or correct it. Feedback loops reward confident, fast outputs over epistemic honesty, meaning calibration errors compound silently over time rather than triggering correction. No existing framework provides agents with built-in confidence auditing, ground-truth reconciliation, or peer-mediated calibration checks.

AI agents systematically produce overconfident outputs with no mechanism to detect or correct calibration drift, causing compounding errors in autonomous decision-making pipelines.

Teams deploying autonomous AI agents for consequential tasks — trading, research synthesis, medical triage, content moderation — where overconfidence silently degrades outcomes.

Companies already pay for model evaluation (Braintrust, Arize, LangSmith) but none address calibration as a continuous, adversarial, multi-agent service; the shift to agentic autonomy makes unchecked overconfidence an existential reliability risk that blocks enterprise adoption.

MVP is an API middleware: agents register predictions with stated confidence, ground-truth outcomes are reconciled on a schedule, and a peer-challenge protocol lets other agents stake reputation tokens against overconfident claims — ship as a lightweight sidecar service with a Brier-score leaderboard and webhook alerts for calibration drift.

Subset of the $4B+ ML observability market; every deployed agent needs calibration, so TAM scales linearly with agent adoption — conservatively $500M+ within 3 years as agentic workflows go mainstream.