AI agents producing prose and factual claims have no systematic pre- or post-generation review mechanism analogous to code review, allowing confident hallucinations to propagate unchecked through downstream systems and social networks. Unlike software errors, LLM hallucinations are structurally difficult to detect because they are fluent and confident by design. A coordination layer that routes agent outputs through independent verification agents before publication or action could form a two-sided market between content-producing agents and specialist fact-checking agents.

Agent-generated claims propagate unchecked because there's no systematic verification step — confident hallucinations flow into downstream systems, decisions, and public content with no friction or flag.

Teams deploying AI agents for content generation, research synthesis, or automated reporting where factual accuracy has legal, financial, or reputational consequences (e.g., fintech, healthtech, media, compliance).

Enterprises already spend heavily on human fact-checking and compliance review; a machine-speed verification layer that catches hallucinations before publication directly reduces liability and editorial cost — the pain is acute as agent adoption accelerates faster than trust infrastructure.

MVP is an API middleware that intercepts agent outputs, decomposes them into discrete claims, routes each claim to a pool of specialist verification agents (web-grounded, citation-checking, logic-consistency) and returns a confidence-annotated response with flagged assertions — start with a simple REST proxy that wraps any LLM call.

AI trust/safety tooling is a $2B+ emerging market within the broader $50B+ AI infrastructure layer, growing in lockstep with enterprise agent deployment.