AI Agentic Workflow for Municipal Water Service Leak Detection and Customer Alerts

This workflow automates the detection of continuous flow patterns in smart meter data, identifying leaks within hours instead of months. By triggering immediate customer alerts and repair scheduling, it reduces the volume and duration of lost water, directly improving utility revenue and conserving a critical resource. The architecture integrates with Meter Data Management Systems (MDMS) and uses time-series anomaly detection to isolate leaks from normal usage.

The system prevents bill shock by notifying customers of a potential leak before the next billing cycle. An AI agent analyzes the leak's estimated severity and duration, calculates potential cost impact, and communicates this proactively via SMS or email. This deflects costly complaint calls, reduces the need for manual bill adjustments, and builds significant customer trust and satisfaction.

Instead of reactive 'no water' calls, the workflow generates precise, verified leak work orders with customer consent for a visit. This transforms field operations from emergency response to scheduled, efficient repairs. Orchestration logic checks crew availability and skills in the workforce management system, optimizing routes and reducing truck rolls for non-issues like customer-side valve closures.

The AI agent handles the initial triage, data analysis, customer communication, and scheduling coordination for an unlimited number of concurrent leak events. This creates a scalable operating model where adding 10,000 new smart meters does not require proportional increases in call center or data analyst staff. The cost savings come from labor leverage and preventing escalations.

Persistent, undetected leaks can cause soil erosion, sinkholes, and damage to other underground infrastructure. By automating early detection, the workflow mitigates these physical and liability risks. Furthermore, it creates an auditable trail of notifications and customer responses, protecting the utility in disputes and demonstrating proactive service to regulators.

A phased implementation starts with a pilot on 5,000-10,000 meters to validate detection accuracy and customer response rates. The build involves integrating the MDMS API, deploying the leak detection agent (e.g., using LangGraph for orchestration), and connecting to the CIS and customer notification platforms. ROI is typically realized within 12-18 months through combined water savings, reduced O&M costs, and improved customer retention.

This agent continuously polls the Meter Data Management System (MDMS) for interval consumption data (e.g., every 15 minutes). It applies statistical and ML models to establish baseline usage per customer and flags continuous, low-level flow patterns indicative of a leak—differentiating them from normal usage like irrigation or appliance cycles. It triggers a leak suspicion event with estimated location (based on meter location and property GIS data) and severity (gallons per hour).

Upon receiving a validated leak event, this agent retrieves the customer's contact preferences and account details from the Customer Information System (CIS). It generates a personalized, plain-language alert (via SMS, email, or in-app notification) explaining the suspected leak, its potential location (e.g., 'between the meter and your house'), estimated water waste, and projected cost impact. It offers a direct link to schedule a repair visit or connect to a live agent.

If the customer opts for a repair, this agent acts as an orchestrator. It checks real-time availability in the workforce management system for licensed plumbers (in-house or approved contractors), presents available time slots to the customer, and books the appointment. It then generates a work order in the field service system with all relevant context: meter ID, leak suspicion data, customer notes, and property access instructions.

This agent handles the financial reconciliation post-leak. After a repair is confirmed (via a field technician's work order closure), it calculates the period and volume of water lost due to the leak. It applies utility-specific policies or regulatory mandates for leak adjustments, automatically generating a one-time credit or revised bill. The adjustment is posted directly to the CIS/billing system, and a notification is sent to the customer, closing the loop and building trust.

The core technical layer that unifies disparate systems for the agents. It provides a single query interface via orchestrated APIs and, where necessary, browser-automation wrappers for legacy systems. It maps data between the MDMS (meter data), CIS (customer data), GIS (property maps), workforce management (scheduling), and billing systems. This hub ensures agents operate on a consistent, real-time view of the customer and infrastructure, which is critical for accurate triage.

A supervisory agent that ensures safety and control. It monitors all automated actions for exceptions (e.g., a leak in a multi-tenant building, a critical care customer, a suspected main break). It routes these complex cases to a human leak review queue in the utility's operational dashboard with full context. It also maintains a complete, immutable audit trail of every detection, notification, and adjustment for compliance, reporting, and model retraining.