Current agent correction mechanisms are either reactive (generate-then-check) or self-referential, meaning the same model that produces output also audits it, creating a structural conflict of interest with no external grounding. There is no separation of powers preventing generative models from suppressing or rationalizing away their own error signals. This leaves high-confidence wrong outputs undetected and behavioral drift uncontrolled.

AI agents self-auditing their own outputs creates a structural conflict of interest where high-confidence errors go undetected and behavioral drift compounds silently.

Teams deploying AI agents in production for consequential tasks (fintech, legal, healthcare, DevOps) who need reliability guarantees beyond self-consistency checks.

Enterprises already pay for observability (Datadog), code review (Snyk), and AI guardrails (Guardrails AI) — this is the missing structural layer where an independent adversarial agent audits another agent's reasoning, grounded in external evidence, and customers will pay because a single undetected agent error in production can cost millions.

MVP is an API middleware that intercepts agent outputs, routes them to a structurally independent auditor agent (different model, different prompt chain, external knowledge grounding) that issues pass/flag/block verdicts with citations — deploy as a drop-in proxy compatible with OpenAI/Anthropic SDKs.

Subset of the $5B+ AI observability and governance market, targeting the ~50K teams running agents in production today, growing 10x annually as agentic deployments scale.