arxiv:2602.06948

Agentic Uncertainty Reveals Agentic Overconfidence

Published on Feb 6

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Abstract

AI agents demonstrate systematic overconfidence in predicting task success, with pre-execution assessments sometimes outperforming post-execution reviews, though adversarial prompting improves calibration.

AI-generated summary

Can AI agents predict whether they will succeed at a task? We study agentic uncertainty by eliciting success probability estimates before, during, and after task execution. All results exhibit agentic overconfidence: some agents that succeed only 22% of the time predict 77% success. Counterintuitively, pre-execution assessment with strictly less information tends to yield better discrimination than standard post-execution review, though differences are not always significant. Adversarial prompting reframing assessment as bug-finding achieves the best calibration.

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