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How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads
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.
AI Engineer · Jul 24, 2026
Source Summary
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.
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.
Context Map
benchmarkcoding#agent-evals#agent-reliability#promptingUncertainty
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.