From Digital Twins to Autonomous Local Agents: Rethinking AI for Smart Water Networks

Internet of Things (IoT) enabled Water Distribution Networks (WDNs) increasingly rely on Digital Twins (DTs) to support monitoring and decision-making, yet most DTs remain passive dashboards. Deploying Artificial Intelligence (AI) within these critical infrastructures requires efficient, autonomous solutions at the edge, where operational constraints and data sovereignty concerns limit the suitability of proprietary cloud-based services that expose sensitive telemetry beyond internal boundaries. At the same time, Large Language Models (LLMs) impose significant computational and energy demands, making lightweight and locally executable agentic pipelines essential for practical deployment under edge constraints. This work introduces a modular multi-agent framework that provides an on-demand DT service capable of executing complex “what-if” hydraulic analyses directly at the network edge. The system employs an autonomous agentic Retrieval-Augmented Generation (RAG) pipeline that enables locally deployed LLMs to interpret user intents, retrieve domain-specific context, generate simulation workflows, and self-correct execution errors without human intervention. This multi-agent design yields robust and trustworthy behavior even under strict resource constraints. Beyond introducing the framework, this work systematically assesses its trustworthiness and feasibility for edge deployment by benchmarking task reliability, hardware requirements, and energy efficiency. Evaluation across a representative benchmark of WDNs shows that the proposed agentic approach improves full-task success rates by up to 30-40% over zero-shot prompting and reduces execution failures by more than 50%. Moreover, 4-bit quantized local models deliver performance competitive with proprietary cloud solutions while lowering peak VRAM usage and energy consumption by over 60%. These results demonstrate the viability of effective, efficient, and trustworthy AI agents operating entirely on edge hardware to securely and autonomously enable advanced IoT infrastructure services.