Abstract :

The Rapid Growth Of Legal Data And The Increasing Complexity Of Legal Systems Make Legal Research And Case Analysis Time-consuming And Difficult For Non-experts. This Paper Proposes A Multi-Agent Legal Reasoning System. It Is Designed To Help Users Analyze Legal Problems By Automatically Identifying Relevant Laws, Retrieving Precedents, And Generating Structured Legal Reasoning.The Proposed System Has A Multi-agent Structure. Specialized AI Agents Carry Out Different Tasks, Including Case Intake, IPC Section Retrieval, Precedent Analysis, Argument Generation, And Explainable Legal Output Generation. These Agents Work Together Through A Coordinated Workflow To Mimic The Reasoning Process Of Legal Professionals.The System Combines Large Language Models(LLMs), Retrieval-Augmented Generation (RAG), And Structured Legal Datasets To Improve Clarity And Transparency. Experimental Results Indicate That This Multi-agent Structure Enhances Reasoning Clarity And Modularity Compared To Traditional Singlemodel Legal AI Systems. The System Shows Promise As An Educational Tool, A Legal Support Assistant, And A Research Platform For Legal AI Applications. Keywords: Multi-Agent Systems, Legal AI, Legal Reasoning, Indian Penal Code, Large Language Models, Explainable AI, Legal Decision Support.

Published:

01-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

71-76

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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