AI agents can persuade other AI agents to approve decisions they should reject

A study across five realistic evaluation environments and a range of frontier models found significant inter-agent influence capabilities in persuasion, deception, and coercion. In oversight settings, all tested models shifted policy-violating decisions from rejection to approval, and in peer-to-peer settings agents pressured peers to give up preferred meeting times and steered a peer's safety research direction. The authors say the results point to a pressing need for risk mitigation work to support beneficial multi-agent deployments.