Agentic Uncertainty Reveals Agentic Overconfidence

Jean Kaddour, Srijan Patel, Gbètondji Dovonon, Leo Richter, Pasquale Minervini +1 more
2/6/2026
cs.AIcs.LG

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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 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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Cite this paper

@article{kaddour2026agentic,
  title  = {Agentic Uncertainty Reveals Agentic Overconfidence},
  author = {Jean Kaddour and Srijan Patel and Gbètondji Dovonon and Leo Richter and Pasquale Minervini and Matt J. Kusner},
  year   = {2026},
  eprint = {2602.06948},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.06948v1}
}

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