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How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads
Start with a few core tasks, inspect traces, change one component at a time, and protect a separate test set.
AI EngineerAI EngineerAI EngineerJul 24, 20262 min
Source Summary
Production agent behavior should be improved through a loop of tool design, small eval sets, trace review, and calibrated judges—not prompt tweaks based on one bad run.
Practical Implication
Build evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.
Agent-Ready Context
The YouTube Ads team recommends optimizing a focused set of agent tools before larger evals, then starting with **a few core tasks** and testing both desired behavior and prohibited actions. Explanations help diagnose multi-dimensional outcomes such as accuracy and brand safety. Build evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent. Do not tune against isolated nondeterministic failures. Keep a sparingly used **test set**, refresh it with production data, monitor judge disagreement, and define launch gates before iteration makes the rubric a moving target.
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