Many real-world AI applications involve more than a single model call. Tasks often require multiple steps, such as retrieving information, processing data, generating outputs, validating results, and triggering external actions. Managing these steps effectively requires well-designed AI workflows.

An AI workflow organizes these steps into a structured sequence of operations. Instead of relying on a single prompt to solve a complex task, the system breaks the problem into smaller stages that are executed in a controlled and repeatable manner.

In this chapter, we will explore how to structure AI workflows, common workflow patterns used in production systems, and the tools that help orchestrate complex AI pipelines.