Author(s): Michalzarnecki

Originally published on Towards AI.

Building an AI Debate Panel: Agents that Argue and Give a Final Conclusion

A single LLM prompt or a plain ReAct (reasoning & take actions) agent often gives you a plausible answer – sometimes great, sometimes off. A panel of specialized agents (Researcher, Expert, Critic) coordinated by a discussion moderator consistently yields clearer reasoning, fewer blind spots, and more actionable outcomes. In this hands-on guide, you’ll build such a panel using LangChain + LangGraph, wire in web search, and stream the debate to your console — ending with a structured, moderator-produced conclusion.

This article discusses the construction and benefits of a multi-agent debate panel utilizing AI agents with specific roles such as Researcher, Expert, and Critic, moderated by a Supervisor to ensure effective interaction and decision-making. It outlines various tools for building this setup, including LangChain and LangGraph, while explaining the advantages of a moderated approach over traditional single-agent interactions, such as improved reasoning, structured outputs, and the ability to address complex questions effectively.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI