Making waves: A conceptual framework exploring how large language model-based multi-agent systems could reshape water engineering

作者：Seyed Hossein Hosseini, Babak Zolghadr‐Asli, Henrikki Tenkanen, Kaveh Madani, Mir A. Matin, İbrahim Demir, Avi Ostfeld, Vijay P. Singh, Dragan Savić · 发表于：Water Research · 年份：2025 · DOI：10.1016/j.watres.2025.125157 · 被引用次数：5 · 研究领域：Multi-Agent Systems and Negotiation、Multimodal Machine Learning Applications、Maritime Navigation and Safety

Large Language Model-based Multi-Agents (LLM-MAs) are emerging systems that manage complex tasks with specialized and coordinated agents. In this paper, we present new perspectives on the integration of LLM-MA systems into enhancing water engineering practices. Water engineering typically involves data integration, analysis, modeling, decision-making, and cross-disciplinary collaboration, which often present significant difficulties. To address these domain-specific complexities, we explore how LLM-MA systems can support advanced operations in water engineering and facilitate them. By pointing out the linguistic capabilities of LLMs and the modular, scalable, and collaborative architecture of LLM-MA systems, we investigate the role of intelligent agents in enabling timely, adaptive, and traceable solutions. Various practical applications were identified, e.g., LLM-MA for pressure drop detection in water distribution networks, flood management, or in their role as potential negotiating agents to find a balanced solution considering differing goals. Our investigation highlights both the capabilities and limitations of LLM-MAs in water engineering and proposes practical recommendations for their effective implementation within the field. This study seeks to develop a foundational framework for understanding how LLM-MAs can shape the future of water engineering processes.