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

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.

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Published via Towards AI