AI agents are powerful, but they are also unpredictable. An agent might choose the wrong tool, misinterpret instructions, loop endlessly, or produce incorrect results while sounding confident. These issues become even more serious when agents interact with real systems such as databases, APIs, or production workflows.

Because of this, building agents is not just about making them capable. It is also about making them reliable and observable.

Reliable agent systems require mechanisms to monitor behavior, detect failures, and diagnose problems. Developers need visibility into what the agent is doing at every step: the prompts it generates, the tools it calls, the reasoning steps it takes, and the outputs it produces.

In this chapter, we will explore how to design agent systems that are easier to debug, monitor, and improve, along with practical techniques for making AI agents more reliable in real-world applications.