Common pitfalls in AI agent evaluation suites can produce misleading results

AI agent evaluation dashboards often present misleading metrics that fail to catch critical errors. A support bot might appear fluent and polite while providing factually incorrect information, such as promising incorrect refund periods. Common issues include evaluators missing skipped tool calls due to incomplete conversation rendering and grading systems penalizing correct guardrail responses. The author recommends implementing deterministic tool-use checks, case-specific evaluation profiles, and separating metrics into hard requirements and graded categories to improve accuracy.