Agentic Uncertainty Reveals Agentic Overconfidence

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.