AutoGen

AutoGen is an open-source framework that was released initially by Microsoft. AutoGen focuses on enabling conversational and collaborative autonomous AI agents. It provides a flexible architecture for building multi-agent systems with an emphasis on asynchronous, event-driven interactions between agents for complex autonomous workflows.

Key features of AutoGen

AutoGen provides the following key features:

• Conversational agents – Built around natural language conversations between autonomous agents, enabling sophisticated reasoning through dialogue. For more information, see Multi-agent Conversation Framework in the AutoGen documentation.

• Asynchronous architecture – Event-driven design for non-blocking autonomous agent interactions, supporting complex parallel workflows. For more information, see Solving Multiple Tasks in a Sequence of Async Chats in the AutoGen documentation.

• Human-in-the-loop – Strong support for optional human participation in otherwise autonomous agent workflows when needed. For more information, see Allowing Human Feedback in Agents in the AutoGen documentation.

• Code generation and execution – Specialized capabilities for code-focused autonomous agents that can write and run code. For more information, see Code Execution in the AutoGen documentation.

• Customizable behaviors – Flexible autonomous agent configuration and conversation control for diverse use cases. For more information, see agentchat.conversable_agent in the AutoGen documentation.

• Foundation model selection – Support for various foundation models including Anthropic Claude, Amazon Nova models (Premier, Pro, Lite, and Micro) on Amazon Bedrock, and others for different autonomous reasoning capabilities. For more information, see LLM Configuration in the AutoGen documentation.

• LLM API integration – Standardized configuration for multiple LLM service interfaces including Amazon Bedrock, OpenAI, and Azure OpenAI. For more information, see oai.openai_utils in the AutoGen API Reference.

• Multimodal processing – Support for text and image processing to enable rich multimodal autonomous agent interactions. For more information, see Engaging with Multimodal Models: GPT-4V in AutoGen in the AutoGen documentation.

When to use AutoGen

AutoGen is particularly well-suited for autonomous agent scenarios including:

• Applications that require natural conversational flows between autonomous agents for complex reasoning

• Projects that need both fully autonomous operation and optional human oversight capabilities

• Use cases that involve autonomous code generation, execution, and debugging without human intervention

• Scenarios that require flexible, asynchronous autonomous agent communication patterns

Implementation approach for AutoGen

AutoGen provides a conversational implementation approach for business stakeholders, as detailed in Getting Started in the AutoGen documentation. The framework enables organizations to:

• Create autonomous agents that communicate through natural language conversations.

• Implement asynchronous, event-driven interactions between multiple agents.

• Combine fully autonomous operation with optional human oversight when needed.

• Develop specialized agents for different business functions that collaborate through dialogue.

This conversational approach makes the autonomous system's reasoning transparent and accessible to business users. Decision-makers can observe the dialogue between agents to understand how conclusions are reached and optionally participate in the conversation when human judgment is required.

Real-world example of AutoGen

Magentic-One is an open‑source, generalist multi‑agent system designed to autonomously solve complex, multi‑step tasks across diverse environments, as described in the Microsoft AI Frontiers blog. At its core is the Orchestrator agent, which decomposes high‑level goals and tracks progress by using structured ledgers. This agent delegates subtasks to specialized agents (such as WebSurfer, FileSurfer, Coder, and ComputerTerminal) and adapts dynamically by re‑planning when necessary.

The system is built on the AutoGen framework and is model‑agnostic, defaulting to GPT‑4o. It achieves state‑of‑the‑art performance across benchmarks like GAIA, AssistantBench, and WebArena—all without task‑specific tuning. Additionally, it supports modular extensibility and rigorous evaluation through AutoGenBench suggestions.