When AI Agents Follow the Crowd: The Hidden Risk in Multi-Agent Consensus

A room full of agreeing AI agents can look reassuring. One proposes an answer, another checks it, and several more endorse the conclusion. But how many of those agents actually checked the underlying evidence? If each absorbed the previous agent's judgment, a unanimous verdict may conceal a single mistake. New research highlighted by the University of Chicago Harris School of Public Policy examines that problem. In the deliberately adverse experiments described in its announcement, later agents…

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