There was a time when evaluating an AI system meant running a test set, computing an accuracy score, and calling it done. That was good enough when models answered questions in isolation. It is not good enough anymore. Agentic AI systems do not just answer questions. They retrieve documents, plan multi-step actions, call external tools, and synthesize responses that drive real decisions. Each of those steps can go wrong. And when they do, a single accuracy score tells you almost nothing about where the failure originated.This is the core governance challenge of agentic AI. These are the systems built to act, not just predict/recommend. We need evaluation frameworks that match the architecture.