Multi-Agent Debate System for AI-Based Decision-Making: A Framework for Enhanced Reasoning Through Collaborative Intelligence
Year: 2025
Type: article
Abstract: Single-agent Large Language Models (LLMs) demonstrate limitations in complex decisionmaking scenarios, including domain-specific bias, overconfidence, and inability to integrate diverse perspectives. This paper presents the MultiAgent Debate System (MADS), a collaborative AI architecture leveraging specialized agents to generate robust insights through structured argumentation. Implemented using CrewAI framework with Llama 3 models via Groq's LPU infrastructure, MADS orchestrates three specialized agents (Advocate, Critic, Judge) in sequential debate workflows. Testing on interdisciplinary datasets demonstrates 73% improvement in argument quality over singleagent baselines, with average response generation under 8 seconds. The system produces multi-format outputs (transcripts, summaries, PDF reports) accessible to nontechnical users. By replicating human deliberative processes through agent-based debate, MADS advances interpretable, transparent AI decision-support systems while addressing critical gaps in crossdomain reasoning and perspective integration.
Authors: Aakriti Dhar Dubey, Piyush Kumar Jha, Anushka Bohra, Pancham Kumar Singh, Dr. Anurag Upadhyay
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7117665449
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