AutoGen
A Microsoft framework for building multi-agent conversational systems where agents interact through natural language messages. AutoGen supports flexible conversation patterns including group chats, nested conversations, and human participation.
AutoGen takes a conversation-first approach to multi-agent systems. Agents communicate by sending messages to each other in structured conversations, similar to how humans collaborate in group chats. The framework supports various topologies: two agents debating, a group of specialists discussing a problem, or hierarchical conversations where a manager agent delegates to workers.
For teams exploring multi-agent architectures, AutoGen's conversational model is intuitive and flexible. It excels at tasks that benefit from iterative refinement through dialogue, like code review, content editing, or strategic planning. The framework supports both fully automated conversations and human-in-the-loop patterns where a person can inject guidance at any point. AutoGen's main strength is its flexibility in defining conversation flows. The consideration for production use is that conversation-based coordination can be unpredictable, so you need robust termination conditions and cost controls to prevent agents from chatting indefinitely.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.