Getting an AI agent to behave the way you want isn't just about writing better prompts. In real systems, behavior emerges from a loop: prompts, evals, iteration, and feedback. Small changes in any part of that loop can completely change outcomes. The Google team shares lessons from building a seed-asset agent that turns messy advertising creatives — low-quality images, cluttered visuals, and heavy text overlays — into clean, reusable assets for downstream generative AI tools. They explain why prompting alone did not produce stable behavior, how evals became feedback signals rather than scorecards, how agent trace logs exposed why failures happened, and how they iterated without breaking problems they had already fixed. Speakers: Chris Souza — Google Chris works on the Google team behind this seed-asset agent and its evaluation workflow. Preetika Bhateja — Product Manager, Google/YouTube Preetika works on ads, evaluations, agents, and LLM-as-judge systems. Daniel Bump — Engineer, Google Daniel focuses on image and video generation and computer vision. X/Twitter: https://x.com/DanielJBump LinkedIn: https://www.linkedin.com/in/danielbump