Building AI Agents with Tool Use and Function Calling

Large Language Models (LLMs) are incredibly powerful, but their knowledge is frozen in time and they can't interact with the outside world. To build true AI agents that can solve complex problems, we need to give them access to tools. This is where function calling comes in.

Tool use, in the context of AI, is the ability for a model to utilize external resources to accomplish a task. This could be anything from searching the web for up-to-date information to executing a piece of code or calling an API. Function calling is the mechanism that enables this interaction.

This guide will provide a comprehensive overview of building AI agents with tool use and function calling. We'll cover the fundamental concepts, explore the different approaches taken by major AI players like OpenAI, Anthropic, and Google, and dive into the practical aspects of building robust and reliable tool-using agents.

What is Function Calling?

At its core, function calling allows a developer to define a set of custom functions that an LLM can choose to execute during a conversation. Instead of just generating text, the model can output a structured JSON object containing the name of a function to call and the arguments to pass to it.

This is a significant leap forward from simple text-in, text-out models. It transforms the LLM from a passive information generator into an active participant that can reason about when and how to use external tools to achieve a goal.

How Function Calling Works

The general workflow for function calling is as follows:

1. Step 1: Define Functions
   The developer provides the LLM with a list of available functions, including their names, descriptions, and parameter schemas.
2. Step 2: User Prompt
   The user provides a prompt or question.
3. Step 3: Model Prediction
   The LLM analyzes the prompt and determines if it needs to call one of the defined functions to provide an accurate response. If so, it generates a JSON object specifying the function and its arguments.
4. Step 4: Function Execution
   The developer's code receives this JSON object, executes the specified function with the provided arguments, and gets a result.
5. Step 5: Model Response
   The result of the function call is then passed back to the LLM, which uses this information to generate a final, more informed response to the user.

This loop can be repeated multiple times, allowing the agent to chain together multiple tool calls to solve complex problems.

Function Calling Patterns: OpenAI, Anthropic, and Google

While the core concept is the same, the major AI providers have slightly different implementations of function calling.

Feature	OpenAI	Anthropic	Google (Vertex AI)
Tool Definition	JSON Schema	Custom XML-like format or JSON	OpenAPI v3 Specification
Invocation	tool_calls in response	tool_use content block	function_call in response
Parallel Calls	Supported	Supported	Supported
Streaming	Supported	Supported	Supported

OpenAI's approach is widely considered the industry standard and is well-documented. It uses JSON Schema to define functions, which provides a familiar and powerful way to describe complex data structures.

Anthropic's implementation is similar but uses a custom XML-like syntax for tool definitions in its earlier models, though recent updates have added JSON support. It emphasizes a conversational approach where the model and user can collaborate on tool use.

Google's Vertex AI leverages the OpenAPI v3 specification for defining tools. This is a powerful choice for developers already using OpenAPI for their APIs, as it allows them to seamlessly integrate their existing services with AI agents.

Common Tool Categories for AI Agents

Tool-using agents can be equipped with a wide range of capabilities. Here are some of the most common categories:

• Search: Accessing real-time information from the web or internal knowledge bases.
• Database: Querying and retrieving data from SQL or NoSQL databases.
• API Interaction: Interacting with external services like weather APIs, flight trackers, or e-commerce platforms.
• Code Execution: Running Python code or shell scripts to perform calculations, manipulate data, or interact with local files.
• Human-in-the-loop: Pausing execution and asking for human input or approval before proceeding.

Architecture Patterns for Tool-Using Agents

There are several common architectural patterns for building tool-using agents:

• Single-tool Agent: The simplest pattern, where an agent has access to a single, well-defined tool.
• Router Agent: This agent acts as a dispatcher, receiving a user query and routing it to the appropriate sub-agent or tool based on the user's intent.
• ReAct (Reasoning and Acting): A popular framework where the agent cycles through a loop of thought, action, and observation. The agent reasons about the problem, chooses an action (a tool to use), and then observes the result to inform its next thought.
• Multi-agent Systems: Complex tasks can be broken down and assigned to a team of specialized agents that collaborate to achieve a common goal.

"The future of AI is not a single, monolithic model, but a swarm of specialized agents, each an expert in its domain, collaborating to solve problems beyond the scope of any single mind." – Chris Dixon

Error Handling and Safety Considerations

Building reliable tool-using agents requires careful consideration of error handling and safety.

• Invalid Tool Calls: The LLM might hallucinate a tool that doesn't exist or provide invalid arguments. Your code should gracefully handle these cases and provide feedback to the model.
• Tool Execution Errors: The tool itself might fail. For example, an API could be down or a database query could time out. Your agent should be able to recover from these errors and potentially try a different approach.
• Prompt Injection: Users might try to trick the agent into executing malicious code or accessing unauthorized resources. It's crucial to validate user input and limit the agent's capabilities.
• Data Privacy: Be mindful of the data you pass to external tools and LLMs. Avoid sending sensitive information unless absolutely necessary.

When to Use Tool Calling vs. Other Approaches

Tool calling is a powerful technique, but it's not always the best solution. Here's a quick comparison:

• Fine-tuning: If your task is highly specific and requires domain knowledge not present in the base model, fine-tuning might be a better option.
• Retrieval-Augmented Generation (RAG): If your goal is to answer questions over a large corpus of documents, RAG can be more efficient than giving the agent a search tool.
• Tool Calling: Use tool calling when you need to interact with the outside world, access real-time information, or perform actions that require external computation.

Propelius Experience Building AI Agents for Clients

At Propelius, we've been at the forefront of building custom AI agents for our clients. We've developed a range of solutions, from simple chatbots that can answer questions about a company's products to complex, multi-agent systems that can automate entire business processes.

Our team has extensive experience with all the major function calling implementations and can help you choose the right tools and architecture for your specific needs. We also place a strong emphasis on building robust and reliable agents that can handle errors gracefully and operate safely and securely. Read more about our work on our blog.

FAQs

What is the difference between tool use and function calling?

Tool use is the general concept of an AI model using external resources, while function calling is the specific mechanism that enables this interaction.

Can I use multiple tools with a single agent?

Yes, you can define multiple functions and the LLM will choose the most appropriate one to call based on the user's prompt.

How do I handle authentication with external APIs?

You should store API keys and other sensitive credentials securely and not include them directly in your prompts. The function that calls the API should be responsible for retrieving and using the necessary credentials.