This was great! When you think about optimizing prompts with GEPA (or comparable methods and tools), do you consider each tool or skill separately? How do you think about the optimization of the system prompt for a large agentic system?

I imagine that you do a collection of passes to cover each overlapping set of what you want evaluated, but the system prompt makes all cases dependent on each other.

What I’ve done in the past is use the system prompt to extract subjective criteria for an LLM judge (like various system prompt statements that contribute to brand voice) and check individual traces with that for evaluation, but I’d like to move beyond including that in a prompt at all.