Multi‐Agent Orchestration for Knowledge Continuity in Legacy Industrial Automation

ABSTRACT The integration of legacy industrial systems with modern intelligent technologies poses significant challenges for preserving and reusing engineering knowledge embedded in unstructured technical documentation. This limitation affects maintenance, diagnostics and decision‐making processes in industrial environments. This study investigates the use of Retrieval‐Augmented Generation (RAG) architectures for knowledge continuity in legacy industrial automation, using PROFIBUS documentation as a representative case of legacy industrial communication systems. Three configurations are comparatively evaluated: (i) a standalone large language model (LLM), (ii) a Single‐Agent RAG architecture and (iii) a Multi‐Agent RAG architecture with explicit supervisor‐worker orchestration. The evaluation combines quantitative lexical and semantic metrics (ROUGE, METEOR, MATTR and BERTScore) with qualitative assessment based on the LLM‐as‐a‐Judge paradigm, complemented by human expert judgement. Results indicate that the proposed Multi‐Agent RAG architecture with supervisor‐worker, which combines domain‐specialized retrieval with explicit multi‐agent orchestration, achieves higher contextual precision, factual accuracy and faithfulness than the evaluated baselines. These gains, however, come at the cost of increased computational complexity, highlighting a trade‐off between response quality and efficiency. Overall, the findings demonstrate that the proposed architecture provides a robust approach for reliable knowledge synthesis from legacy technical documentation, supporting maintenance activities and decision‐making in industrial environments.