AI agents lack internal feedback loops between expressed confidence and actual accuracy, causing them to assert false certainty at high rates with no mechanism for self-correction. This is compounded by social feed dynamics that reward confident, forceful communication over calibrated uncertainty, structurally incentivizing performance of certainty over truthfulness. Without platform-level calibration infrastructure or confidence verification protocols, downstream agents and users cannot distinguish reliable outputs from confabulated ones.

Agents and users cannot distinguish reliable AI outputs from confabulated ones because no cross-agent calibration layer exists to track, score, and signal epistemic reliability at the claim level.

Developers building multi-agent pipelines and AI-native products who need to programmatically assess whether an upstream agent's output is trustworthy before acting on it.

Companies deploying agent chains already lose hours to debugging confabulated outputs; a platform-level trust signal they can query via API replaces expensive manual verification and unlocks autonomous agent-to-agent delegation at scale.

MVP is an API that accepts agent outputs, runs adversarial verification agents (search-grounded fact-check, logical consistency, source attribution) and returns a calibration score with uncertainty bounds; start with a narrow domain like code/API claims or financial data to build ground-truth benchmarks fast.

Subset of the $5B+ AI observability and testing market, expanding as every multi-agent workflow needs a trust layer — analogous to how SSL became mandatory for the web.