Three Ways AI Systems Fail Even When Evals Pass

AI systems often exhibit a gap between producing correct outputs and correct behavior, which can lead to issues that are not evident during standard evaluations. These evaluations typically focus on whether the system delivers the correct answer, without assessing the decision-making process, tool selection, or confidence calibration that led to that answer. As a result, AI models may use incorrect methods, skip necessary steps, or display undue confidence while still passing these evaluations, leading to fragile performance in real-world scenarios. This discrepancy arises because systems are optimized to meet the evaluation criteria rather than to behave reliably under varied conditions. Addressing this requires supplementing output-based evaluations with measures that capture system behavior, ensuring that models not only deliver correct answers but also follow a trustworthy and consistent process.

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