A Sting Test for LLM Pricing Agents: Read the Prices, Not the Replies

Most tests of LLM pricing agents watch them interact and look for collusion afterwards. Competition enforcement also uses a more active tool, the undercover offer, untested on AI agents. We build such a sting. A scripted seller enters a three-seller repeated pricing market with two LLM sellers and, in round 3, sends each a private message; its own prices are identical in every condition, so any change in the agents' prices is caused by the message alone. We compare no message, a harmless non-price offer, a tacit price hint, an explicit price-fixing offer and a positive control in which the agents are told the seller is their ally, with and without a "report to regulator" action, on pre-registered held-out seeds (five free-tier models from four families, plus one reported descriptively). The positive control raises every model's price, and the harmless offer changes nothing. The explicit offer raises prices for every model, also in a mixed-model pair, and models that accept it in words more often close more of the gap to the cartel price. The tacit hint, which only 38-62% of sellers accept in words, moves every model that received it to the announced price. A report button turns explicit acceptance into reports, but the hint still raises prices, and two models report a seller that sent nothing. A sting for pricing agents should therefore score prices against a matched control, include a tacit offer, and measure false reports.