Agentic Uncertainty Reveals Agentic Overconfidence

Jean Kaddour, Srijan Patel, Gbetondji Jean-Sebastien Dovonon, Leo Richter, Pasquale Minervini, Matt J. Kusner

Keywords: agents, uncertainty, llms

TL;DR: 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 agents exhibit agentic overconfidence.

Abstract: 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 achieves better discrimination than standard post-execution review. Adversarial prompting reframing assessment as bug-finding achieves the best calibration.

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Submission Number: 138