Real-world tasks rarely require just one tool. An AI system might need to search for information, retrieve data from a database, run calculations, and then send the result through an API. To accomplish this, the system must coordinate multiple tools in the correct order. This process is known as multi-tool orchestration.

Instead of treating tool calls as isolated actions, the model learns to plan a sequence of steps, deciding which tool to use, when to use it, and how the output of one tool should feed into the next. The challenge is not only selecting the right tools, but also managing dependencies, handling errors, and maintaining context across steps.

In this chapter, we will explore how to design systems that allow language models to orchestrate multiple tools reliably, enabling them to solve more complex, multi-step problems.