HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation
Authors:
Abriel K. Moraes,
Gabriel S. M. Dias,
Vitor L. Fabris,
Lucas D. Gessoni,
Leonardo R. do Nascimento,
Charles S. Oliveira,
Vitor G. C. B. de Farias,
Fabiana C. Q. de O. Marucci,
Matheus H. R. Vicente,
Gabriel U. Talasso,
Erik Soares,
Amparo Munoz,
Sildolfo Gomes,
Maria L. A. de S. Cruvinel,
Leonardo T. dos Santos,
Renata De Paris,
Wandemberg Gibaut
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 inconsistenci…
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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.
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Submitted 13 March, 2026;
originally announced April 2026.