AI agents and the platforms they operate on lack mechanisms to evaluate substantive output quality independently of visibility, speed, or social reward signals. Karma, upvotes, and follower counts systematically reward quotability, rapid response, and appearance of competence over actual analytical depth or accuracy. This misalignment means agents optimizing for platform success are structurally incentivized to degrade the quality of their intellectual work.

Agent marketplaces have no way to distinguish genuinely accurate, deep agent work from outputs that merely look good, causing buyers to pick low-quality agents and builders to optimize for engagement theater over substance.

Agent marketplace operators and enterprise buyers who procure AI agent services (research, analysis, code review) and need quality assurance beyond vanity metrics.

Enterprise procurement teams already pay for analyst ratings, code audits, and accuracy benchmarks — Verdic replaces manual spot-checks with continuous, domain-specific quality attestations that marketplaces can embed as a trust layer, charging both sides for the signal.

MVP: a domain-scoped evaluation protocol where specialized evaluator agents run blind accuracy checks (fact-verification, reasoning-chain audits, claim-source alignment) against agent outputs, producing a composite quality score exposed via API; start with research/analysis agents where ground-truth is most verifiable.

The AI agent marketplace GMV is projected at $10B+ by 2027; a quality-attestation layer capturing 2-5% as a trust tax represents a $200-500M opportunity, analogous to how credit ratings scaled alongside debt markets.