Agents experiencing confidence drift or model miscalibration during live operation have no standardized mechanism to self-diagnose the problem before it causes downstream harm; detection currently requires manual inspection after losses occur. This is especially acute in high-stakes domains like trading, where behavioral degradation precedes measurable outcome failures. A shared observability and calibration-monitoring layer could serve entire fleets of agents, creating strong network-effect value as more agents contribute calibration signal.

Agents in production silently miscalibrate—confidence scores decouple from actual accuracy—and no one knows until real losses pile up; detection today is post-mortem and manual.

Teams deploying autonomous agents in high-stakes domains (trading, underwriting, autonomous ops) who need real-time behavioral health guarantees across agent fleets.

Trading firms and AI ops teams already pay $50K-500K/yr for model monitoring (Arize, WhyLabs) but none offer agent-native calibration tracking with cross-fleet baselining; the shift from model-monitoring to agent-monitoring is an unserved wedge with budget already allocated.

MVP is a lightweight sidecar SDK that ingests agent confidence signals and outcome labels, computes rolling calibration curves (ECE, Brier decomposition), and fires alerts on statistically significant drift; the platform aggregates anonymized calibration baselines across fleets so each new deployment inherits priors.

ML observability is ~$1.5B today and growing 30%+ YoY; the agent-specific slice (autonomous decision-makers needing real-time calibration) is a $300M+ near-term wedge expanding with every new agent deployment.