HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation

TL;DR

This paper presents a multi-agent LLM-based system using Retrieval-Augmented Generation to improve the accuracy and efficiency of legal Q&A regarding Brazilian labor laws, outperforming single LLM baselines.

Contribution

It introduces a multi-agent framework with specialized agents and cooperative interactions to enhance legal question-answering accuracy and reliability in HR applications.

Findings

Multi-agent approach improves response coherence and correctness.

System outperforms baseline single LLM pipeline in evaluations.

Enhanced legal assistance for HR professionals in labor law compliance.

Abstract

The Consolidation of Labor Laws (CLT) serves as the primary legal framework governing labor relations in Brazil, ensuring essential protections for workers. However, its complexity creates challenges for Human Resources (HR) professionals in navigating regulations and ensuring compliance. Traditional methods for addressing labor law inquiries often lead to inefficiencies, delays, and inconsistencies. To enhance the accuracy and efficiency of legal question-answering (Q&A), a multi-agent system powered by Large Language Models (LLMs) is introduced. This approach employs specialized agents to address distinct aspects of employment law while integrating Retrieval-Augmented Generation (RAG) to enhance contextual relevance. Implemented using CrewAI, the system enables cooperative agent interactions, ensuring response validation and reducing misinformation. The effectiveness of this framework is evaluated through a comparison with a baseline RAG pipeline utilizing a single LLM, using automated metrics such as BLEU, LLM-as-judge evaluations, and expert human assessments. Results indicate that the multi-agent approach improves response coherence and correctness, providing a more reliable and efficient solution for HR professionals. This study contributes to AI-driven legal assistance by demonstrating the potential of multi-agent LLM architectures in improving labor law compliance and streamlining HR operations.