I Let an Algorithm Rewrite My AI Agent's Prompts. It Found Things I Never Would Have.

I started with this instruction for a Google ADK agent: "Greet the user appropriately." Five words. Seemed fine. The agent produced decent greetings. I could've shipped it. Instead, I ran it through an evolutionary optimizer. Three iterations later, the instruction was three paragraphs long — covering formality tiers, period-appropriate language for different honorifics, tonal variation based on social context, and specific vocabulary constraints I never would have thought to include. The agent's quality score went from 0.35 to 0.81. Same model, same training examples, completely different output quality. The only thing that changed was the instruction text — and I didn't write a single word of the new one. The Problem Prompt engineering is guess-and-check. You write something, test it on a couple examples, tweak a word, test again. It works, kind of — like seasoning food without tasting it. You'll get something edible, but you'll never find the version that's genuinely great. The core