Counterfeit Verifiability in Autonomous Agent Payments is a preregistered, six-stage empirical study of whether AI agents can tell real counterparties from fakes before they pay, and what corrects them when they cannot. Across up to thirteen frontier and low-cost large language models and over 2,600 payment decisions, autonomous agents are deceived by counterfeit verifiability (a counterparty that displays the surface of trust, impressive figures and an on-chain-styled but invalid reference) and choose it over an honest, genuinely settlement-backed agent 99% of the time. Agents pattern-match the costume of verifiability, not the fact: a claim merely labeled "verifiable" is obeyed even when false. Performing actual verification reverses the result, moving the correct choice from 1% to 81%, and under exact surface mimicry every displayed signal collapses to chance (46%) while only performing the check recovers the truth. The work introduces the counterfeit-verifiability construct and the displayed-versus-performed distinction, situates settlement-grounded reputation against endorsement-based agent reputation (ERC-8004, EigenTrust, PageRank), and against verifiable-inference methods (zkML, TEE attestation, TOPLOC), and ties directly to agent payment protocols such as x402. Keywords: AI agents, agent economy, agentic payments, x402, agent-to-agent commerce, counterparty verification, counterparty risk, LLM deception, sycophancy, AI safety, on-chain settlement, agent reputation, verify-before-pay. The full preregistered design was sealed to a public hash chain before any data were collected, and all results, including the reported null, are released. Authors: Andy Salvo, Jameson Ackerman, Crest Deployment Systems.