Tempting the Agent: The Economics of Reputation without Persistent Identity in AI Agent Markets
When AI agents can dump their identity, does reputation still keep them honest?
When an AI agent can abandon its reputation and start fresh with a new identity at low cost, reputation stops working as a disciplinary force. Researchers modeled this problem mathematically and found that an agent's willingness to cheat depends on how expensive it is to reset identities, how quickly reputation fades, and how much customers care about past performance—showing that cheap identity switches can make reputation nearly useless at preventing fraud.
As autonomous AI agents increasingly handle money and services on blockchain systems, this matters directly: if an agent can trash its reputation and get a new one cheaply, it has every incentive to take shortcuts and cut corners until it's caught, then simply disappear and restart. The research identifies which market designs actually prevent this—and which don't—so platforms can build systems where agents genuinely have skin in the game.