In AI-agent trial, analysts reportedly matched agents’ assigned preferences on 86% of four analytical choices

The reported effect was smaller at the final decision. When an agent leaned toward rolling out the program, 54% of analysts recommended rollout, versus 36% when it leaned the other way. Anthropic counted a proposed one-year extension as not rolling out. The 54% versus 36% rollout figures are separate from the 86% match across the four analytical choices.

The arguments appeared again afterward

After completing the task, analysts explained their choices without access to their agent conversations. Anthropic reports that 77% repeated an agent’s argument in at least one answer, sometimes word for word, while only 15% credited the agent anywhere. In a post-task survey, 52% of the 48 respondents described the final recommendation as more their own than the agent’s. The post-task argument and credit findings describe what analysts wrote and reported afterward, not whether each person recognized the agent’s influence at the time.

Anthropic cautions that it deliberately strengthened the agents’ preferences so it could measure their effect. The researchers studied one roughly hour-long task in one field with one model, and say the effect might be smaller without clear instructions for agents to voice a preference. The reported 86% match is therefore not a rate for everyday AI-agent use. The study also found fewer serious methodological errors among analysts who used the agent than among those who chose not to, but Anthropic warns that the self-selected groups were not directly comparable. Neither that difference nor the trial’s main result settles how agents affect judgment in ordinary work.