AI Agent Confidence Scores Are Not Reliable Probabilities, Experts Warn

A technical analysis published on DEV Community warns that self-reported confidence scores from AI agents are not calibrated probabilities but simply another token output generated by the same model producing the answer. Because the same neural network that may have hallucinated a response also assigns it a high confidence rating, the score provides no independent verification of accuracy. The article argues that routing or auto-approval logic built on these scores creates a false sense of rigor, as a fabricated answer and a correct one are indistinguishable to the model from within. Instead, the author recommends a tiered evaluation framework prioritizing externally verifiable signals — such as whether a file exists or code compiles — over model-generated self-assessments. Self-reported confidence is placed at the least trustworthy end of the evaluation hierarchy, described as more misleading than having no confidence signal at all.