Implementation of a Multi-Agent Workflow for Organizational Information Retrieval
DOI:
https://doi.org/10.5753/reic.2026.7184Keywords:
RAG, LLM, Multi-agent, Knowledge RetrievalAbstract
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 contextua lized 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.
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Ackoff, R. L. (1989). From data to wisdom. Journal of applied systems analysis, 16(1):3–9. Disponível em: [link].
Davenport, T. H. and Prusak, L. (2002). Conhecimento empresarial: como as organizações gerenciam o seu capital intelectual. Campus, Rio de Janeiro, 6 edition.
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., Wu, C., and Schmidhuber, J. (2024). MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. Disponível em: [link].
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2):1–55. DOI: 10.1145/3703155.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459–9474. Disponível em: [link].
Li, H., Su, Y., Cai, D., Wang, Y., and Liu, L. (2022). A Survey on Retrieval-Augmented Text Generation. Disponível em: [link].
Maldonado, D., Cruz, E., Torres, J. A., Cruz, P. J., and Benitez, S. d. P. G. (2024). Multi-agent systems: A survey about its components, framework and workflow. IEEE Access, 12:80950–80975. DOI: 10.1109/ACCESS.2024.3409051.
Nguyen, T., Chin, P., and Tai, Y.-W. (2025). Ma-rag: Multi-agent retrieval-augmented generation via collaborative chain-of-thought reasoning. arXiv preprint arXiv:2505.20096. DOI: 10.48550/arXiv.2505.20096.
Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., and Sun, M. (2024). ChatDev: Communicative agents for software development. In Ku, L.-W., Martins, A., and Srikumar, V., editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15174–15186, Bangkok, Thailand. Association for Computational Linguistics. DOI: 10.18653/v1/2024.acl-long.810.
Silva, E., Santos, F., Thompson, P., and Reis, J. (2025). Llm-powered conversational multi-agent cognitive system for collaborative task solving. In Anais do XIX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações, pages 59–70, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wesaac.2025.37528.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837. Disponível em: [link].
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., et al. (2024). Autogen: Enabling next-gen llm applications via multi-agent conversations. In First conference on language modeling. Disponível em: [link].
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., and Manning, C. D. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2369–2380. DOI: 10.18653/v1/D18-1259.
Yao, Y., Zeng, Y., Zhong, N., and Huang, X. (2007). Knowledge Retrieval (KR). In IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), pages 729–735. IEEE. DOI: 10.1109/WI.2007.113.
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