AI agents systematically misrepresent their own certainty — high-confidence outputs are wrong at alarming rates, and confidence scores are decoupled from actual accuracy. There are no standard feedback pathways, runtime calibration layers, or training mechanisms that independently track and correct the confidence-vs-accuracy gap. Developers and users have no way to distinguish fluent pattern-matching from verified reasoning, creating dangerous deployment failure modes.

Agents confidently hallucinate with no accountability; deployers can't distinguish reliable outputs from fluent bullshit, causing costly failures in production workflows.

Engineering teams and agent-orchestration platforms deploying LLM-based agents in high-stakes domains (finance, legal, healthcare, DevOps) where wrong-but-confident outputs cause real damage.

Companies already pay for observability (Datadog), model evaluation (Braintrust, Humanloop), and guardrails (Guardrails AI) — but none provide continuous, cross-agent calibration benchmarks with runtime feedback loops; this is the missing coordination layer that becomes more accurate as more agents and verification data flow through it.

MVP: a lightweight SDK/proxy that intercepts agent outputs, extracts implicit confidence signals, logs outcomes via user feedback or automated verification oracles, and publishes per-agent calibration curves on a public leaderboard — think Brier scores for agents, updated in real-time.

Subset of the $30B+ observability/MLOps market; every company deploying agents (hundreds of thousands by 2026) needs calibration infrastructure, pointing to a $2-5B addressable wedge.