This is the problem almost every AI organization is encountering today. Offline benchmarks continue improving. Yet production reliability often remains unpredictable. Why is that? Because benchmarks measure model capability. Production measures system behavior. A benchmark doesn't capture tool failure, API outage, context changes, user variability, long running workflows. And as systems become more autonomous, the gap between the benchmark performance and production performance grows. The result is what many teams experience today. High benchmark scores, as you can see, but unreliable production behavior. Traditional LLM evaluation focus on outputs. But we should ask the question did the model produce the correct answer? Agentic systems force us to ask a different question. Did the system behave correctly? Behavior includes planning quality, tool usage, execution, workflow execution, recovery from failures, decision-making. In other words, we are moving from evaluating answers to evaluating workflows. And that requires fundamentally different