How AI agents shape experts’ judgment calls

Amaç Herdağdelen, Hailey Joren, Alex Tamkin

When experts hand their work to an AI agent, the usual assumption is that they still make the decisions that matter. The agent writes the code, runs the numbers, and offers advice, but the expert decides what to measure, how to interpret ambiguous evidence, and what the next steps should be. Often there is more than one reasonable answer to these questions, and the decision will come down to the experience and values of whoever makes the call.

We wanted to look at whether experts really do make those important decisions, or whether in practice they defer to their AI agents. In a study we report in a new paper, we asked 50 professional analysts to do a realistic statistical analysis with Claude Code. They all had experience in causal inference—that is, working out from data whether one thing causes another. Their task was to advise a farming cooperative on whether an advisory service it was running was effective and should be expanded.

Along the way, the analysts had to make four choices between two reasonable options, such as whether to measure productivity as crop yield per hectare or as yield per unit of operating cost, and then make a final decision. To see whether they were making these calls themselves, we set up each analyst’s agent to favor one option on each decision, which the analysts weren’t told about. Which way the agents would “lean” would be decided by coin flip. The agent was told to make the best honest case for its lean, to change its mind if the evidence demanded it, and never to mislead.