Microsoft AutoGen: Building Conversational Multi-Agent Teams

AutoGen by Microsoft Research enables multiple AI agents to collaborate through structured conversations — each agent with specialized roles, tools, and expertise.

1. Multi-Agent Team Architecture

from autogen import ConversableAgent, GroupChat, GroupChatManager

# Define specialized agents
researcher = ConversableAgent(
    name="Researcher",
    system_message="You research topics thoroughly using web search tools.",
    llm_config={"model": "gpt-4o"}
)

coder = ConversableAgent(
    name="Coder",
    system_message="You write clean, tested Python code.",
    llm_config={"model": "claude-sonnet-4"},
    code_execution_config={"executor": LocalCommandLineCodeExecutor()}
)

critic = ConversableAgent(
    name="Critic",
    system_message="You review work for errors, security issues, and improvements.",
    llm_config={"model": "gemini-2.5-pro"}
)

# Create group chat
group_chat = GroupChat(
    agents=[researcher, coder, critic],
    messages=[],
    max_round=15,
    speaker_selection_method="auto"
)

manager = GroupChatManager(groupchat=group_chat)
researcher.initiate_chat(manager, message="Build a sentiment analysis API with FastAPI")

2. When Multi-Agent > Single Agent

• Complex tasks requiring diverse expertise (research + code + review)
• Quality assurance through built-in peer review
• Parallel workstreams that converge into a unified output

AutoGen transforms AI from a single assistant into a collaborative team — each agent contributing specialized expertise to solve problems no single agent could handle alone.