Uma metodologia baseada em agentes com avaliação autônoma para extração de conhecimento em textos técnicos

Abstract

In the current scenario of Operational Technology (OT) and Information Technology (IT) convergence and Industry 5.0, the maintenance and resilience of OT systems depend on knowledge continuity. The difficulty and inefficiency in accessing the dispersed technical knowledge within unstructured documentation of legacy systems, such as the PROFIBUS (Process Field Bus) protocol, critically impact maintenance and decision-making. Therefore, this work investigates the use of Generative Artificial Intelligence architectures to retrieve and synthesize this legacy industrial knowledge. The study compares the performance of three distinct configurations: (i) a standalone LLM (Baseline), (ii) a Single-Agent RAG (Retrieval-Augmented Generation) model, and (iii) a Multi-Agent RAG architecture. Through an experiment based on PROFIBUS technical texts, performance was evaluated using quantitative metrics (ROUGE, BERTScore) and qualitative assessment (LLM-as-a-Judge). The results demonstrate that Multi-Agent orchestration establishes a superior performance hierarchy. The collaboration and specialization among agents significantly increase the contextual precision and factual accuracy of the responses, while simultaneously reducing hallucination to 80% faithfulness to the context. The study validates the Multi-Agent RAG as a necessary architectural pattern to guarantee reliability in the transfer of engineering knowledge, providing a pathway for human-centric automation in Industry 5.0.