AI agents fail differently from ordinary request-response LLM features. An agent might choose the wrong tool, misread a tool error, repeat an unproductive action, exceed its budget, or produce a confident final answer from incomplete evidence. These failures matter more when the agent can touch databases, APIs, files, browsers, or customer-facing workflows.

Reliability is not something you add after the prompt "works." It is part of the architecture: hard limits, typed tool interfaces, permission checks, tracing, evals, rollback paths, and human escalation.

Developers need visibility into what the agent actually did: which model was called, which messages were sent, which tools were requested, what arguments were used, what the tools returned, how much it cost, and why the run stopped. Without that record, debugging becomes guesswork.

This chapter focuses on the engineering controls that make agents debuggable and safer to operate in real systems.