Multi-Agent Chatbot Systems

Single-agent systems are often insufficient for complex, multi-step tasks. Multi-Agent Systems (MAS) involve multiple specialized agents collaborating to achieve a goal. This mimics a human team structure.

Why Multi-Agent?

• Specialization: One agent can be a "Coder," another a "Reviewer," and another a "Product Manager." This allows using different system prompts and even different LLMs for each role (e.g., GPT-4 for coding, Llama 3 for summarizing).
• Robustness: Agents can critique and correct each other's work (Self-Reflection and Peer-Review).
• Scalability: Complex workflows can be broken down into smaller, manageable sub-tasks.

Orchestration Patterns

1. Joint Chat (Round Robin)

Agents speak one after another in a loop. Good for brainstorming or simple collaboration.

2. Hierarchical Chat (Manager-Worker)

A "Manager" agent breaks down the task and assigns sub-tasks to "Worker" agents. The Manager aggregates the results. This is essential for complex projects.

Frameworks: AutoGen vs. CrewAI

AutoGen (Microsoft)

AutoGen is highly flexible and event-driven. It models agents as "ConversableAgents" that can send and receive messages. It supports:

• Human-in-the-loop: The user can intervene at any step.
• Code Execution: Agents can write and execute Python code in a Docker container.

CrewAI

CrewAI is more structured and role-based. You define a "Crew" of agents with specific:

• Role: e.g., "Senior Researcher"
• Goal: e.g., "Uncover the latest trends in AI"
• Backstory: Gives the agent personality and context.

Code Example: AutoGen

Here is how you might set up a simple two-agent system where a User Proxy asks an Assistant to plot a chart.

from autogen import AssistantAgent, UserProxyAgent, config_list_from_json

# Load LLM config
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")

# Create Assistant Agent (The Coder)
assistant = AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list}
)

# Create User Proxy Agent (The Executor)
user_proxy = UserProxyAgent(
    name="user_proxy",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={"work_dir": "coding", "use_docker": False}
)

# Start the chat
user_proxy.initiate_chat(
    assistant,
    message="Plot a chart of NVDA and TSLA stock price change YTD. Save the plot to a file."
)

Challenges

• Infinite Loops: Agents might get stuck thanking each other or repeating the same error.
• Context Window: Long conversations can exceed the token limit. Memory management (summarizing past turns) is crucial.
• Cost & Latency: Multiple agent calls increase token usage and response time significantly.

Conclusion

Multi-agent systems represent the next frontier in AI automation. They enable us to move from simple "chatbots" to powerful "agentic workflows" that can perform real work.