Building an AI application that works in a demo is easy. Building one that works reliably in production is much harder. Models can produce incorrect outputs, APIs can fail, prompts can break with small changes, and external tools may return unexpected results. Without proper safeguards, these issues quickly turn into unreliable user experiences.

In this chapter, we explore how to design AI systems that remain dependable even when things go wrong.