# How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads

Source: [AI Engineer](https://www.youtube.com/watch?v=xyL2Ltkh-SA)  
Feed7 permalink: https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01  
Published: 2026-07-24T00:00:00.000Z  
Trust: Source Linked (source_linked)

## Why Included

Start with a few core tasks, inspect traces, change one component at a time, and protect a separate test set.

## 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.

## Context Map

- Layer: benchmark
- Domains: coding
- Topics: agent-evals, agent-reliability, prompting

## Uncertainty

- None recorded.

## Agent Instruction

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