When language models interact with tools, letting them freely decide every step can work for simple tasks, but it often becomes unreliable for complex systems. Real production applications usually require predictable and structured workflows.

Instead of relying entirely on the model’s reasoning at every step, we can define explicit workflows that guide how tools are used. The model may still make decisions within the workflow, but the overall structure ensures the system behaves consistently and avoids unnecessary errors.

This approach is common in production AI systems where tasks follow clear stages such as retrieval, validation, processing, and response generation.

In this chapter, we will explore how to design structured workflows around tool usage, allowing language models to operate within controlled pipelines while still leveraging their reasoning abilities.