Function Calling and Tool Use

Function Calling and Tool Use

Function calling is a capability that lets AI models interact with external systems by calling predefined functions. Instead of just generating text, the model can decide to call a function, pass the required parameters, and use the result to continue its response. This is what makes AI agents possible.

The way it works is straightforward. You define a set of functions with names, descriptions, and parameter schemas. When you send a prompt to the model, you also include the function definitions. If the model determines that a function would help answer the query, it outputs a special response indicating which function to call and with what parameters, instead of generating regular text.

Your application then executes the function and sends the result back to the model. The model incorporates the result into its response. This cycle can repeat, with the model calling multiple functions in sequence to accomplish complex tasks. For example, a travel agent model might call a flight search function, a hotel booking function, and a weather forecast function to plan a trip.

Models are trained to handle function calling by being fine tuned on examples of function usage. The best models can handle complex function calls with many parameters, understand when to call multiple functions, and gracefully handle errors when functions fail. This capability is a key differentiator between models, with frontier models generally offering the most reliable function calling.

Function calling is the foundation of modern AI applications. It powers coding assistants that can read and write files, customer service bots that can access databases and update records, and research assistants that can search the web and compile information. Combined with agentic programming patterns, function calling transforms AI from a text generator into an active participant in your workflow.