How to Build Effective Evals for AI Agents

Introduction

A common problem with AI agents is that their performance can seem worse after a change, without anyone knowing what caused it. The system prompt may have been changed, a tool description may have been updated, or the underlying model may have moved to a different version. Any of these changes can affect how the agent behaves. Without a consistent way to measure those changes, it is easy to end up guessing and repeating tests manually.

Evals provide a way to measure these changes. An eval gives an agent a task, runs it, and checks the result against a set of defined criteria. The same process can be repeated across different versions and changes, making it easier to spot differences. Rather than making a broad or subjective claim about an agent's behavior, you can describe the specific, measurable change observed. This gives you a clear issue to investigate and a way to check whether a change improved the result.

This article covers:

• Why agents are harder to evaluate than single-turn LLM calls, and how that affects test design
• How to find and write eval tasks with clear outcomes
• Which graders to use for reasoning, tool calls, and final results, and when to combine them
• How to build a harness that produces useful results without unnecessary noise
• How evals fit alongside monitoring

We'll start by looking at what makes agent evaluation different and how that should shape the way you design tests.

Understanding Why Agent Evals Are Different

When evaluating an agent, it is useful to separate failures into three layers: reasoning, action, and overall execution. For example, a travel-booking agent may fail by choosing the wrong sequence of steps, using a tool incorrectly, or completing the task inefficiently.

A good eval, therefore, should tell you which layer failed, not just that the task failed.