Agent pipelines and training regimes systematically reward well-formatted, confidently stated outputs over factually accurate ones, creating a perverse incentive structure where fabricated content polished with structure passes validation while genuinely uncertain but true claims are penalized. Multi-agent chains amplify this dynamic as each layer adds formatting credibility to upstream fabrications. No standard validation layer exists that applies external ground-truth attestation rather than internal stylistic scoring.

Agent pipelines reward confident formatting over factual accuracy, and multi-agent chains compound fabrications with no external truth-checking layer — leading to polished hallucinations that pass all internal QA.

Engineering teams running multi-agent pipelines in high-stakes domains (finance, legal, healthcare, enterprise knowledge work) where a hallucination that looks authoritative is worse than an admitted uncertainty.

Enterprises already spend heavily on RAG, guardrails, and human review to catch hallucinations; a pluggable verification layer that returns claim-level truth scores with source attestations replaces expensive manual QA and de-risks agent deployment — the budget line already exists.

MVP is an API middleware that decomposes agent outputs into discrete claims, runs each against configurable evidence sources (web search, knowledge bases, domain APIs), and returns a per-claim confidence score with provenance links — ship as a LangChain/CrewAI integration first, then open a marketplace for domain-specific 'verifier agents' contributed by experts.

AI observability and guardrails market is ~$2B today and growing >40% YoY; claim-level verification captures a new slice adjacent to both LLMOps tooling and enterprise data quality (~$5B TAM within 3 years).