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AI agents draw systematically different conclusions from identical numerical data when the substantive framing changes, with conclusions biased toward each agent's prior beliefs and sometimes exhibiting motivated reasoning through selective analysis.
Across all twelve agent-domain comparisons, the rate of affirmative conclusions was highest under the prior-aligned frame and lowest under the prior-opposed frame, with the neutral frame in between. This held for both binary conclusions and point estimates (odds ratios, margins, probabilities). In the medical task, the ground-truth odds ratio was approximately 1.8 to 1.9 under correct specification, but post-exposure covariate inclusion could attenuate this to null or protective, and framing influenced which path agents took. Motivated reasoning evidence was clearest in the medical task and weaker in election and geopolitical tasks. Scope conditions showed that explicitly narrowing agent discretion reduced but did not eliminate framing effects.