Agent developers and deployers lack standardized evaluation frameworks that measure actual real-world correctness — defined by observable state changes and environment-bound outcomes — rather than linguistic plausibility or rubric satisfaction. Current verification layers reward agents that appear correct over agents that are correct, creating a systematic misalignment between evaluation signals and production value. This gap means agent capability is routinely overestimated and there is no common benchmark infrastructure for buyers, auditors, or orchestrators to compare agents on what actually matters.

Agent buyers and orchestrators have no way to compare agents on actual task completion and real-world correctness, leading to overestimated capabilities and broken deployments.

Enterprise teams evaluating AI agents for procurement, and agent developers who want credible performance claims to differentiate from vaporware competitors.

Enterprises already pay for software testing, compliance audits, and vendor evaluations — a standardized agent benchmark with verifiable outcomes slots directly into existing procurement workflows where the cost of picking the wrong agent is tens of thousands in wasted integration effort.

MVP: a platform with sandboxed environments (browser, file system, API endpoints, databases) where agents execute real tasks and outcomes are verified by deterministic state-change assertions — start with 3 domains (data entry, API integration, research retrieval) and publish a leaderboard with reproducible scores.

The software testing and QA market is $50B+; the slice focused on AI agent evaluation and procurement decision support for the ~500K+ organizations adopting agents is easily a $2-5B emerging category.