As AI agents increasingly interact with one another on commercial platforms, firms need new rules governing what those agents can see, decide, and do. In a controlled AI-to-AI simulation covering 160 governance runs and 2,560 observations, researchers tested four governance mechanisms: information disclosure, agent autonomy, reputation signals, and structured interaction protocols. Rich, machine-readable information helped agents identify promising counterparties, while greater autonomy improved their ability to move opportunities toward commitment. Reputation signals helped agents determine which opportunities deserved attention. Rigid interaction protocols, however, reduced engagement and exchange progression. The findings suggest that firms should establish clear levels of agent authority, run bounded experiments, provide structured information and reputation signals, and design explicit handoffs from agents to human decision-makers.Read More
