Validating a UCP manifest takes a second. Scoring it for agent-readiness takes another. Neither of those answers the harder question: when a real frontier agent — Claude or GPT or Gemini , picked by a user three weeks from now — walks up to your store with an ordinary shopping prompt, does it actually complete a checkout? Compared to the next implementation? Across the models people are actually using? Today there's no shared way to find out. AI commerce has the same coordination problem ML had before MLPerf, web performance had before Lighthouse, and coding models had before HumanEval — and the cost of not solving it is the same: every claim a vendor makes about agent-readiness is currently unverifiable by anyone outside that vendor. This post is about what we've been building to close that gap. The pre-benchmark moment Every category that grew up around AI has gone through a pre-benchmark moment. Machine learning before MLPerf was a pile of vendor-flavoured numbers.…