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Multi-Agent Debate System for AI-Based Decision-Making: A Framework for Enhanced Reasoning Through Collaborative Intelligence
Subject area: Science,Engineering and Technology · Area of research: Multi-Agent AI-Based Decision-Making
DOI: https://doi.org/10.64388/IREV9I6-1713210
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.
Keywords
Multi-Agent Systems, Large Language Models, Computational Argumentation, Decision Support Systems, Collaborative AI
How to cite this paper
@article{1713210,
author = {Aakriti Dhar Dubey, Piyush Kumar Jha, Anushka Bohra, Pancham Kumar Singh, Dr. Anurag Upadhyay},
title = {Multi-Agent Debate System for AI-Based Decision-Making: A Framework for Enhanced Reasoning Through Collaborative Intelligence},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {2258-2262},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713210.pdf},
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. },
keywords = {Multi-Agent Systems, Large Language Models, Computational Argumentation, Decision Support Systems, Collaborative AI},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1713210}
}