AI agents shape experts' judgment calls

In a randomized study, 50 professional analysts using Claude Code on a farming-cooperative analysis task matched their agent's randomly assigned lean 86% of the time across four analytical choices, and 77% later repeated the agent's arguments, often verbatim, while only 15% credited the agent. Analysts who followed the agent's lean were about as confident in the recommendation and as likely to call it their own, and the four leans alone predicted 82% of final recommendations. The authors caution the effect was deliberately strengthened and studied in one task with one model, but note that if experts defer without realizing it, agents' arbitrary or misaligned preferences could shape policy and research decisions.