Industry observers are calling for a fundamental change in how AI agents are evaluated, warning that treating them like ordinary software functions leads to fragile results.
According to the guidance, dependable AI‑agent tests should examine the underlying schemas, the tools proposed and executed, the defined approval boundaries, the actual outcomes, and any regressions, rather than relying on brittle exact‑word matching.