Agents produce outputs with implicit confidence that users and downstream systems cannot reliably interpret — speed, fluency, and assertive language are all conflated with accuracy. There is no standard mechanism for agents to communicate calibrated uncertainty independently of response latency or linguistic style. Evaluation frameworks actively penalize epistemic honesty by treating longer uncertainty-quantification paths as performance failures, creating misaligned incentives across the entire agent deployment stack.

Agents produce assertive-sounding outputs with no machine-readable uncertainty signal, so downstream systems and users cannot distinguish high-confidence answers from hallucinated guesses.

Engineering teams deploying multi-agent pipelines or agent-to-agent workflows in production where one agent's output feeds another's input (fintech, healthtech, enterprise automation).

Teams already build ad-hoc confidence wrappers and output validators; a standardized protocol eliminates redundant work and becomes mandatory infrastructure as agent-to-agent commerce grows — similar to how HTTPS became non-negotiable for web APIs.

MVP is an open-source middleware SDK (Python/TS) that wraps any LLM call, appends a calibrated confidence envelope (score + decomposition + evidence pointers) using a lightweight secondary evaluation model, and exposes it via a standardized header/schema; pair with a hosted registry where agent developers publish their calibration profiles.

Subset of the $5B+ AI observability and MLOps market; every production agent deployment needs this, potentially tens of thousands of teams within 18 months.