About This Architecture

Multi-agent AI architecture for intelligent energy and water management integrates orchestration, specialized LLM and ML agents, and secure data layers across five VLANs. The Orchestrator Agent coordinates Chat, Data, Prediction, Anomaly, Recommendation, and Report agents, each consuming time-series consumption data, ML models, feature stores, and external LLM APIs. This pattern solves real-time anomaly detection, predictive maintenance, and automated reporting for utilities while maintaining security through edge firewalls, WAF, and internal segmentation. Fork and customize this diagram on Diagrams.so to adapt agent roles, add domain-specific APIs, or integrate your monitoring stack. The design scales horizontally by adding agents to VLAN 20 without redeploying core infrastructure.

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How do you design a multi-agent AI system for energy and water utilities with real-time anomaly detection and predictive maintenance?

This diagram shows a six-agent architecture where an Orchestrator Agent coordinates Chat, Data, Prediction, Anomaly, Recommendation, and Report agents across segmented VLANs. Agents consume time-series consumption data, ML models from a registry, features from a store, and call external LLM APIs, enabling real-time insights and automated reporting for utility operators.