Agent as Tool

LLMs have the capability to call external tools. Agent as Tool pattern allows an agent to be used as a tool for an LLM to use. This means that an LLM can choose to call an agent when it thinks necessary. This also means that we don't need to always have fixed processes, but ask an LLM to act on its own to finish a task.

We can mix agents and tools to handle various types of tasks.

• If a task has a predefined flow, patterns like Evaluator-Optimizer, Parallelization Workflow, Routing Workflow and Chain Workflow can be used to build an agent to finish this task. This agent leverages other agents to finish the task.
• If a task has no predefined flow or requires creative thinking, we can provide a list of agents as tools to an LLM. The LLM uses these tools to finish the task.

Implementation

To register an agent as a tool for an LLM to use, we can leverage the capability provided by different frameworks. For Spring AI, we can define a Task Execution agent as a Java Function, then register this Function as a Spring bean. These tools can be enabled when using a ChatClient to send the requests to an LLM.

Example

The example is generating Python code to test a REST API. This REST API accepts the User model created in the example of Task Execution pattern. The agent to generate test users is configured as a tool for LLMs to use.

Below is the prompt for an LLM to generate code.

Prompt to generate REST API test

Goal: Write Python code to test a REST API.
        
Requirements:
- The API is responsible for creating a new user.
- Use a POST request with JSON content to create a new user.
- When creation is successful, the API returns a 201 code.
- The API url is http://localhost:8080/api/v1/user.
- User data has a defined structure, don't generate it yourself, use the tool to generate it.

From the generated code, we can see that the LLM uses the user generation agent to generate a test user.

Generate Python code

import requests

# Define the API URL
api_url = "http://localhost:8080/api/v1/user"

# Define the user data
user_data = {
    "id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
    "name": "John Doe",
    "email": "john.doe@example.com",
    "mobilePhone": "+1234567890",
    "addresses": [
        {
            "id": "c9bf9e57-1685-4c89-bafb-ff5af830be8a",
            "addressType": "HOME",
            "countryOrRegion": "US",
            "provinceOrState": "NY",
            "city": "New York",
            "addressLine": "123 Fake Street",
            "zipCode": "10001"
        }
    ]
}

# Make the POST request to create a new user
response = requests.post(api_url, json=user_data)

# Check if the user was created successfully
if response.status_code == 201:
    print("User created successfully.")
else:
    print(f"Failed to create user. Status code: {response.status_code}")