AI Agents: Mastering the Tool Use Design Pattern - Part 4

Hi everyone, Shivam Goyal here! This blog series, based on Microsoft's AI Agents for Beginners repository, continues with a deep dive into the Tool Use Design Pattern. In previous posts (links at the end!), we covered agent fundamentals, frameworks, and design principles. Now, we'll explore how tools empower agents to interact with the world, expanding their capabilities and enabling them to perform a wider range of tasks.

What is the Tool Use Design Pattern?

The Tool Use Design Pattern enables Large Language Models (LLMs) within AI agents to leverage external tools. These tools are essentially code blocks, ranging from simple functions like calculators to complex API calls, that agents execute to perform actions, access information, and achieve goals. Crucially, these tools are invoked through model-generated function calls, allowing the agent to dynamically choose and utilize the appropriate tool for a given task.

Use Cases for Tool Use

The Tool Use Design Pattern unlocks a vast array of applications:

• Dynamic Information Retrieval: Accessing real-time data from external APIs and databases.
• Code Execution and Interpretation: Solving complex problems, generating reports, and running simulations.
• Workflow Automation: Automating multi-step processes and integrating with various services.
• Customer Support: Interacting with CRM systems, ticketing platforms, and knowledge bases.
• Content Generation and Editing: Leveraging tools for grammar checking, summarization, and content evaluation.

Implementing the Tool Use Design Pattern

Key elements for implementing this pattern include:

• Function/Tool Calling: The core mechanism for LLMs to interact with tools.
• Dynamic Information Retrieval: Fetching up-to-date data from external sources.
• Code Execution and Interpretation: Running code and scripts for complex tasks.
• Workflow Automation: Automating processes and integrating services.
• Customer Support: Connecting to support systems and knowledge bases.
• Content Generation and Editing: Utilizing tools to enhance content creation.

Function/Tool Calling in Detail

Function/tool calling is the heart of the Tool Use Design Pattern. It involves a structured process:

1. LLM with Function Calling Support: Use an LLM that supports function calling (e.g., Azure OpenAI).
2. Function Schema: Define a JSON schema describing available functions, including their names, descriptions, and parameters.
   

   {
       "type": "function",
       "function": {
           "name": "get_current_time",
           "description": "Get the current time in a given location",
           "parameters": { 
               // ... parameter definitions ...
           }
       }
   }

3. Function Code: Implement the actual code for each function.
   

   def get_current_time(location):
       # ... code to retrieve current time ...

4. Invocation and Response: The LLM receives a user request, selects the appropriate function based on the schema, and generates a function call with arguments. The function executes, and its response is returned to the LLM, which then formulates a final response to the user. Agentic frameworks like Semantic Kernel simplify this process.

Tool Use with Agentic Frameworks

Semantic Kernel: Simplifies function calling by automatically serializing functions and handling communication between the model and your code. It also provides pre-built tools like File Search and Code Interpreter.

# Example of a Semantic Kernel plugin
from semantic_kernel.functions import kernel_function

class GetCurrentTimePlugin:
    # ... class definition and function implementation ...

Azure AI Agent Service: A managed service offering secure and scalable agent deployment, automatic tool calling, managed data storage (threads), and pre-built tools (Bing Search, Azure AI Search, Function Calling, Code Interpreter, etc.).

# Example of using a toolset in Azure AI Agent Service
toolset = ToolSet()
toolset.add(fetch_data_function)  # Add custom function
toolset.add(code_interpreter)    # Add pre-built tool

Trust and Security Considerations

When working with tools like dynamically generated SQL, address security concerns by configuring appropriate database access permissions (e.g., read-only access) and running the application in a secure environment.

Further Learning and Resources

• Azure AI Agents Service Workshop
• Contoso Creative Writer Multi-Agent Workshop
• Semantic Kernel Function Calling Tutorial
• Semantic Kernel Code Interpreter
• Autogen Tools

Catch up on the series:

• Part 1: Introduction to AI Agents
• Part 2: Exploring Agentic Frameworks
• Part 3: Agentic Design Principles

 If you have any further questions or would like to connect for more discussion, feel free to reach out to me on LinkedIn | GitHub