AI agents fail differently from simple request-response LLM features. An agent might choose the wrong tool, misunderstand a tool error, repeat the same action, exceed its budget, or give a confident 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 to see 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, you are guessing.

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