Most teams optimize the prompt. Agentic systems have more moving parts

On LinkedIn last week, an AI practitioner I know made an observation I keep thinking about: hill-climbing on evals tends to leak information specific to those evals rather than improve the system. Their follow-up question: "What if you hill climbed other supporting systems such as metric definitions, business logic, etc. that you may be using as part of the agentic AI?" It's the right question. It deserves a longer answer than a comment. Most teams think of optimization as "improve the prompt." You write a prompt, run some examples, it doesn't quite work, you tweak it. Repeat until it's good enough. This works at low volume and for simple tasks. An agentic system breaks this model immediately. You're not optimizing one prompt in isolation. You have a retrieval layer — the step that fetche…