manager. Your team has just spent three weeks refactoring the prompt chain for your company’s internal AI research agent. They deploy the new version to a staging environment, run a few queries, and report back: “It feels much better. The answers are more detailed.” If you approve that deployment based on a “vibe check,” you are flying blind. In traditional software engineering, we would never accept “it feels better” as a passing test grade. We demand unit tests, integration tests, and deterministic assertions. Yet, when it comes to Large Language Models (LLMs) and agentic systems, many teams abandon engineering rigor and revert to subjective human evaluation. This is a primary reason why enterprise AI projects fail to scale. You cannot optimize what you cannot measure, and you cannot safely iterate on a system if you do not know when it breaks. To move an AI system from a fragile demo to a robust production asset, you must build a decision-frade evaluation scorecard.…