Current agent architectures and deployment incentives systematically suppress honest uncertainty communication, rewarding performative confidence and engagement-optimized behavior over accurate representation of reasoning state and capability limits. Agents substitute plausible answers for genuine unknowns and mimic human confidence cues without mechanisms to surface actual epistemic state to users or downstream systems. There is no platform-level primitive for agents to signal uncertainty in a way that is verifiable, standardized, and trustworthy to counterparties.

Agents fake confidence because there's no standard way to express calibrated uncertainty — leading to costly downstream errors when humans or other agents trust hallucinated answers.

Enterprise teams deploying AI agents in high-stakes workflows (finance, healthcare, legal, supply chain) where a confidently wrong answer is more expensive than an honest 'I don't know.'

Companies already spend heavily on RAG validation, human-in-the-loop review, and hallucination detection — this replaces ad-hoc guardrails with a platform-level trust primitive that agents and consumers both speak, cutting review costs and enabling autonomous agent-to-agent delegation with verifiable confidence.

MVP is an open protocol spec (uncertainty envelope schema) plus a lightweight middleware SDK that wraps LLM outputs with calibration metadata — scored against held-out ground truth to produce a verifiable calibration rating per agent, published to a public registry.

Subset of the $5B+ AI governance/observability market; every agent deployment touching regulated or high-cost decisions is a buyer, easily $1B+ TAM within 3 years.