Building an AI agent is only the first step. The real challenge is ensuring that it works reliably across many different scenarios. Unlike traditional software, agent behavior can vary depending on prompts, context, tool responses, and even small changes in input. This makes evaluation and testing essential.

Agent evaluation focuses on answering a simple question: Is the agent actually solving the task correctly and consistently?

To answer this, developers need systematic ways to test agent behavior. Instead of relying on a few manual checks, agent systems should be evaluated using structured benchmarks, test scenarios, and measurable metrics.

In this chapter, we will explore practical techniques for evaluating AI agents, designing effective test cases, and building evaluation pipelines that help ensure agents perform reliably in real-world applications.