What Is AutoGen?#

AutoGen is a multi-agent conversation framework from Microsoft Research. Agents communicate through structured messages — think of it as a messaging platform where each participant is an AI agent with specific capabilities.

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from autogen_agent import Agent, ChatAgent, ToolAgent
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from autogen_runtime import Runtime, GroupChat
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class DataAnalyst(ChatAgent):
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    def __init__(self):
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        super().__init__(name="analyst")
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        self.system_prompt = "You analyze data and create visualizations"
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        self.tools = [query_database, generate_chart]
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class CodeReviewer(ChatAgent):
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    def __init__(self):
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        super().__init__(name="reviewer")
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        self.system_prompt = "You review code for bugs and security issues"
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        self.tools = [run_linter, check_security]

Agents register with a Runtime and participate in a GroupChat. Messages flow between agents based on routing rules or designated roles.

The Rebuild: What Changed in 2025#

The original AutoGen (2023-2024) was complex. The autobuild system generated agents dynamically, debugging was painful, and state management was implicit. The 2025 rewrite fixed every major complaint:

The rebuild was controversial — some existing users had to rewrite their agents. But production teams report significantly fewer runtime surprises.

Core Concepts#

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class CustomerSupportAgent(ChatAgent):
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    def __init__(self):
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        super().__init__(name="support")
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        self.system_prompt = """You are an L1 support agent.
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        Handle common issues:
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        - Password reset: use reset_password tool
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        - Billing: escalate to billing_agent
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        - Technical: escalate to tech_agent
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        """
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        self.tools = [reset_password, lookup_account, search_kb]
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    async def on_message(self, msg: Message, ctx: Context):
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        if msg.intent == "billing":
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            await ctx.send_to("billing_agent", msg)
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        elif msg.intent == "technical":
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            await ctx.send_to("tech_agent", msg)
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        else:
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            result = await self.process_with_tools(msg)
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            await ctx.reply(result)

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@dataclass
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class SupportTicket(Message):
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    ticket_id: str
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    customer_email: str
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    issue_type: str  # "billing" | "technical" | "account"
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    description: str
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    priority: int

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chat = GroupChat(
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    agents=[triage, billing, tech, escalation],
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    routing="round_robin",  # or "role_based", "broadcast"
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    max_turns=10,
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    admin_agent=escalation  # can override routing
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)

Code Execution Sandbox#

AutoGen’s code execution is its standout feature. The 2025 version includes a built-in sandbox — no Docker required:

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from autogen_code import CodeExecutionAgent
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coder = CodeExecutionAgent(
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    name="code_runner",
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    language="python",
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    sandbox="built-in",  # or "docker"
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    timeout=30,
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    max_output_size=10000
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)

The built-in sandbox works by:

1. Creating an isolated process per execution
2. Restricting filesystem access to a temp directory
3. Limiting network access (configurable allowlist)
4. Enforcing memory and CPU limits
5. Killing processes that exceed timeouts

For teams that prefer Docker isolation, the Docker sandbox is still available and unchanged.

Azure Integration#

AutoGen is the only framework with native Azure AI integration. This matters for Microsoft shops:

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from autogen_azure import AzureRuntime
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runtime = AzureRuntime(
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    model="gpt-4o",
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    deployment="my-deployment",
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    endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
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    auth="entra_id",
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    content_filter=True
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)

Features:

• Entra ID authentication — No API keys in code
• Content filtering — Azure AI content safety built in
• Audit logging — All agent activity logged to Azure Monitor
• Managed identity — No credential management
• Private networking — Agents can access VNet-protected resources

For enterprises already on Azure, this integration alone justifies AutoGen over other frameworks.

Production Patterns#

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specialist = ChatAgent(name="specialist", tools=[write_code])
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reviewer = ChatAgent(name="reviewer", tools=[run_tests, check_style])
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runner = CodeExecutionAgent(name="runner")
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chat = GroupChat(
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    agents=[specialist, reviewer, runner],
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    routing="role_based"
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)

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agents = [
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    TriageAgent(name="triage", tools=[lookup_account, search_kb]),
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    BillingAgent(name="billing", tools=[query_invoices, process_refund]),
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    TechAgent(name="tech", tools=[check_logs, run_diagnostics]),
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    EscalationManager(name="escalation", human_handoff=True)
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]
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chat = GroupChat(
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    agents=agents,
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    routing="role_based",
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    max_turns=5,
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    admin_agent=escalation
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)

AutoGen vs LangGraph vs CrewAI#

Limitations#

1. Heavy Azure dependency — Features like content filtering and managed identity only work with Azure. OpenAI/Bedrock users miss most enterprise features.
2. Typed messages require upfront planning — You define message schemas before agents. This is good for correctness but slows prototyping.
3. GroupChat can deadlock — Poor routing rules or conflicting agent instructions can cause infinite loops. The max_turns limit is essential.
4. Smaller ecosystem — Fewer community tools and integrations compared to LangChain. You’ll write more custom code.

Next in the Series#

Series: AI Agent Frameworks 2026 — Production Comparison. Post 3: AutoGen. Post 4: Claude Agent SDK → coming next.