Does an Antitrust Collusion Test Work on AI Agents?

As AI agents increasingly transact for users, detecting collusion between them matters, and antitrust economics offers tools built for the same problem among firms. We test one, Bajari and Ye’s (2003) exchangeability test, which assumes competitive firms follow a common bidding strategy and flags pairs whose bids respond differently to public cost factors. We hypothesized that honest agents running the same model would converge on such a strategy, even with memory of past rounds. We built a sealed-bid procurement auction modeled on their market, with six Claude Haiku 5.5 bidders, instructed cartels and a no-collusion control, and ablated memory, bid history and cost information. Despite the model declining instructed bid-rigging in about 95% of cartel messages, false positives in our control were sufficient for assessing the efficacy of the method. In the full environment, the test rejected 32.2% of honest pairs (95% CI 21.7–42.7%) against a nominal 5%. Most rejections involved firms that drifted into habits such as routinely sitting out projects; removing memory and scratchpads lowered the rate to 9.4%. The rate fell to its nominal level (4.4%) only when bid history and the cost formula were also removed and most agents stopped pricing against rivals, a setting that strips much of what colluders would need to coordinate. Identical models did not converge on a common strategy here, suggesting convergence should be checked before applying equilibrium-based detection methods to AI agents.