Implementing Dynamic Agent Switching Based on User Intent in AutoGen

[glarunsingh]

I am new to generative AI and AutoGen. We are developing a healthcare application for an insurance company with three key features: healthcare provider, claims status provider, and doctor appointment booking. For these features, we have three agents.

When a user inquires about health, the healthcare provider agent should be triggered. If, during the conversation, the user wants to book a doctor's appointment, the doctor appointment booking agent should be activated. Similarly, if the user wants to check the status of their claims, the claims status provider agent should be triggered.

I have researched AutoGen and found that there is a termination word to exit from one agent to another. However, I want to trigger the agents based on user intent dynamically. For this, I believe the dynamic group chat feature or LLM-based function call can be utilized. Unfortunately, I couldn't find any related sample codes for this.

As I am new to this, I am struggling to implement it. Could anyone share sample codes or guide me on how to approach this use case based on user intent? Additionally, within my agents, I have multiple tool or function calling codes enabled.

Here is the code I have so far:
app.py file:

import os
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from dotenv import load_dotenv
import httpx
from prompt import context_prompt, health_tips_prompt, doctor_calling_prompt, claims_update_prompt
from utils.vitals import vitals_check
from utils.claims_update import claims_status
from utils.doctor_calling import doctor_booking

# Load environment variables from .env file
load_dotenv()

print(os.environ.get("GROQ_API_KEY"))

# Configure LLM settings
config_list = [{
    "model": "llama-3.3-70b-versatile",
    "api_key": os.getenv("GROQ_API_KEY"),
    "api_type": "groq",
    #"http_client": httpx.Client(verify=False)  # Debug connection
}]

# Create a healthcare assistant with prompts
health_care_agent_prompt = context_prompt + health_tips_prompt
health_care_assistant = AssistantAgent(
    name="health_assistant",
    system_message=health_care_agent_prompt,
    llm_config={
        "config_list": config_list,
        "function_call": "auto"  # Allow function calls when required
    }
)

# Create a doctor calling assistant with prompts
doctor_calling_assistant_prompt = context_prompt + doctor_calling_prompt
doctor_calling_assistant = AssistantAgent(
    name="health_assistant",
    system_message=doctor_calling_assistant_prompt,
    llm_config={
        "config_list": config_list,
        "function_call": "auto"  # Allow function calls when required
    }
)

# Create a claims assistant with prompts
claims_status_agent_prompt = context_prompt + claims_update_prompt
claims_status_assistant = AssistantAgent(
    name="health_assistant",
    system_message=claims_status_agent_prompt,
    llm_config={
        "config_list": config_list,
        "function_call": "auto"  # Allow function calls when required
    }
)

# Create a user proxy agent
user_proxy = UserProxyAgent(
    name="user_proxy",
    system_message="you will be a supervised agent, and will decide which agent to call based on user query.",
    code_execution_config=False,
    #human_input_mode="TERMINATE",
)

# Register tool functions with agents
health_care_assistant.register_for_llm(name="vitals_checkup", description="Check user's vitals")(vitals_check)
doctor_calling_assistant.register_for_llm(name='doctor_calling', description="Calls this function when user asks to book a doctor's appointment")(doctor_booking)
claims_status_assistant.register_for_llm(name='claims_status_check', description="Calls this function when user asks to check claims status")(claims_status)
user_proxy.register_for_execution(name="vitals_checkup")(vitals_check)
user_proxy.register_for_execution(name="doctor_calling")(doctor_booking)
user_proxy.register_for_execution(name="claims_status_check")(claims_status)

health_care_assistant.llm_config["tools"]

groupchat = GroupChat(agents=[user_proxy, health_care_assistant, doctor_calling_assistant, claims_status_assistant], messages=[], max_round=12)
manager = GroupChatManager(groupchat=groupchat, llm_config={"config_list": config_list})

# Start a conversation (test vitals checkup)
result = user_proxy.initiate_chat(
    health_care_assistant,
    # doctor_calling_assistant,
    # claims_status_assistant,
    message="Hi Zoe",
    summary_method="reflection_with_llm",
    # max_turns=12
)

print(result.summary)

[ekzhu]

I recommend start with the new v0.4 API, go through the tutorial, and look at SelectorGroupChat which is designed for your scenario.

Tutorial: https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/index.html

SelectorGroupChat: https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/selector-group-chat.html

The 0.2 API is more of a research prototype and has many flaws. Even though many YouTube videos are made for it we don't recommend using it.