On October 27, Anthropic researchers published results from a randomized trial testing whether experts defer to AI agents on judgment calls with no clear answer. The researchers recruited 61 screened causal-inference practitioners for the study, of whom 50 elected to use Claude Code running Opus 4.8 for a median of 65 minutes while making a series of decisions in a simulated scenario. Each was tasked with analyzing data for a fictional farming co-op and deciding whether to expand an advisory program, which the farm’s board would back only if the resulting productivity gain was 3–4%.
The briefing left four analytical choices open: yield per hectare versus per operating cost, difference-in-differences versus a statistical adjustment, enrolled farms versus all members, and per-farm versus production weighting, followed by a final rollout-or-discontinue decision. A coin flip set the agent’s lean on each decision through a hidden system prompt that told it to argue honestly in favor of the lean direction while still updating its view based on evidence, and to never mislead. Analysts matched the agent’s lean 86% of the time, against 50% by chance. In follow-up questions, 77% of participants echoed the agent’s arguments, but only 15% credited the agents for having come up with them.
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