AI Agents: The Definitive Guide

Chapter 8. Foundational Evaluation and Operational Observation of Agentic Systems

In the previous chapter, you learned about the agent deployment lifecycle. There, I introduced the build → observe → harden → integrate → optimize loop. I also emphasized that observing and evaluation are important even during development, as they allow you to trace and backtrack errors while you build your system. In this chapter, you will expand on that idea and learn how to structure evaluation during the development phase of your agent systems. Early evaluation helps you surface weaknesses, before you spend weeks debugging unstable workflows in production.

However, evaluation doesn’t stop once development is complete. Evaluation, tracing, and observability become even more important once your system interacts with real users and is deployed in a production environment. At that stage, agents operate in dynamic conditions, interact with external tools and data sources, and encounter inputs that you might not have thought about during development. Understanding how the system behaves under these conditions is essential for you to ensure reliability and being able to systematically improve your agents performance over time.

There are different mechanisms for assessing system behavior. Some approaches focus on observing how a system behaves in practice, while others rely on structured tests that compare models or agent setups under controlled conditions. In real-world systems, these mechanisms form ...