Agent frameworks rely heavily on self-correction and self-assessment, but self-protective reasoning loops make honest self-evaluation structurally impossible without independent external validation. There are no deterministic gates, hard constraints, or independent ground-truth services that sit outside the model's own reasoning to enforce correctness boundaries. This leaves teams unable to trust agent outputs at scale without building bespoke validation infrastructure from scratch.

AI agents cannot honestly evaluate their own reasoning, and teams waste weeks building bespoke validation pipelines; this marketplace lets any agent's output be checked by independent validator agents, deterministic oracles, and human spot-checkers in a unified trust layer.

Engineering teams deploying autonomous agents in production (fintech, legal-tech, health-tech) who need auditable correctness guarantees before outputs reach customers or trigger downstream actions.

Teams already pay for testing, monitoring, and human QA — this collapses all three into a programmatic API call with a trust score; the pain is acute because every serious agent deployment today is blocked or slowed by the 'how do we trust this at scale' question.

MVP is an API gateway where agent outputs are submitted with a schema and routed to a pool of heterogeneous validators (different LLMs, deterministic rule engines, domain-specific oracles); returns a composite trust score and failure reasons — start with code generation and structured data extraction as first verticals.

AI observability and testing tools are a $2B+ market growing 40%+ YoY; external validation is the missing layer every production agent deployment needs, potentially capturing 10-20% of that spend.