EPANET-Agentic: A multi-agent system for natural language-controlled simulations of water distribution networks

Water distribution networks (WDNs), a critical part of urban infrastructure, normally require numerous model simulations for effective planning and management. However, traditional WDN modelling requires complex workflows and specialized expertise. EPANET is the most widely adopted modelling tool for WDN hydraulics and water quality simulations, yet its operational complexity restricts accessibility and slows timely decision-making. Recent advances in large language models (LLMs) have led to the development of agentic artificial intelligence systems that autonomously coordinate tasks and control complex engineering simulations through natural language prompts. Here we introduce EPANET-Agentic, a multi-agent system that integrates advanced workflow reasoning with the EPANET simulator and incorporates human-in-the-loop oversight for critical interventions. The new platform adopts an orchestrator-centred, tool-driven architecture that nests three specialised agents (TaskExecutor, CodeRunner, and DataAnalyzer) as function-call tools. This design enables autonomous task decomposition, precise tool invocation, and transparent workflow management. The abilities of EPANET-Agentic are evaluated on three benchmark networks (i.e., L-Town, C-Town, and Net3) across four categories of tasks: System Characteristics, System Dynamics, System Operation, and Scenario Simulation. The results demonstrate that EPANET-Agentic achieved a 100% success rate and tool invocation accuracy with no human interventions. Moreover, the multimodal DataAnalyzer agent provided valid interpretations of simulation results, while the nested tool design ensured robustness and the architecture exhibited strong scalability across diverse hydraulic analysis tasks. These findings confirm that EPANET-Agentic enables natural language-controlled WDN simulation and analysis with engineering-grade reliability, while still adhering to a human-in-the-loop approach required for safety-critical systems. With its modular architecture and strong adaptability, EPANET-Agentic marks a step change from conventional WDN modelling approaches, positioning itself as a next-generation platform for complex planning and management challenges.

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Publication Details

Journal
Water Research
Published
2026-01-21
DOI
https://doi.org/10.1016/j.watres.2026.125433
Primary Topic
Water Systems and Optimization
Type
article
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article

EPANET-Agentic: A multi-agent system for natural language-controlled simulations of water distribution networks

Dragan A. Savic, Jinhua Wang, Guangtao Fu
Water Research
Water Systems and Optimization
article

EPANET-Agentic: A multi-agent system for natural language-controlled simulations of water distribution networks

Dragan A. Savic, Jinhua Wang, Guangtao Fu
article en

Abstract

Water distribution networks (WDNs), a critical part of urban infrastructure, normally require numerous model simulations for effective planning and management. However, traditional WDN modelling requires complex workflows and specialized expertise. EPANET is the most widely adopted modelling tool for WDN hydraulics and water quality simulations, yet its operational complexity restricts accessibility and slows timely decision-making. Recent advances in large language models (LLMs) have led to the development of agentic artificial intelligence systems that autonomously coordinate tasks and control complex engineering simulations through natural language prompts. Here we introduce EPANET-Agentic, a multi-agent system that integrates advanced workflow reasoning with the EPANET simulator and incorporates human-in-the-loop oversight for critical interventions. The new platform adopts an orchestrator-centred, tool-driven architecture that nests three specialised agents (TaskExecutor, CodeRunner, and DataAnalyzer) as function-call tools. This design enables autonomous task decomposition, precise tool invocation, and transparent workflow management. The abilities of EPANET-Agentic are evaluated on three benchmark networks (i.e., L-Town, C-Town, and Net3) across four categories of tasks: System Characteristics, System Dynamics, System Operation, and Scenario Simulation. The results demonstrate that EPANET-Agentic achieved a 100% success rate and tool invocation accuracy with no human interventions. Moreover, the multimodal DataAnalyzer agent provided valid interpretations of simulation results, while the nested tool design ensured robustness and the architecture exhibited strong scalability across diverse hydraulic analysis tasks. These findings confirm that EPANET-Agentic enables natural language-controlled WDN simulation and analysis with engineering-grade reliability, while still adhering to a human-in-the-loop approach required for safety-critical systems. With its modular architecture and strong adaptability, EPANET-Agentic marks a step change from conventional WDN modelling approaches, positioning itself as a next-generation platform for complex planning and management challenges.

Water ResearchVol. 293
University of Exeter (GB), KWR Water Research Institute (NL)
Sustainable cities and communities
Openalex Percentile: Top 82%
Water Systems and Optimization
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