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toolChapter 4arXiv · 2023

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Qingyun Wu (Microsoft Research), Gagan Bansal (Microsoft Research)

Abstract

We present AutoGen, a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

AutoGen is the most enterprise-ready multi-agent framework currently available. The human-in-the-loop integration is particularly important: it provides a natural escalation path when agents reach the boundary of their competence.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Multi-agent conversation framework
  • →Human-agent collaboration model
  • →Customizable agent roles

Topics

multi-agent conversationagent orchestrationhuman-in-the-loopLLM applications