RAG Multiagente para Recuperação de Conhecimento Organizacional - Moretti, Guilherme L.; Sousa, Kalmax dos S.; Mendes, Matheus dos S.; Oliveira, Marcos A. de (Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC), 2025)
This paper proposes a multi-agent system based on Retrieval Augmented Generation (RAG) to support knowledge management in corporate environments. The solution combines semantic document retrieval with response generation using large language models (LLMs), enabling fast and contextualized access to organizational information. By employing domain specialized agents and an intelligent orchestration architecture, the system provides accurate responses tailored to different decision-making levels. This approach aims to overcome challenges posed by the fragmentation and volume of institutional documents, promoting scalability, modularity, and efficiency in internal knowledge retrieval.