Autonomous Outage Cause Analysis and Customer Notification Workflow

This workflow automates the critical, time-sensitive bottleneck of diagnosing outages and informing customers. By correlating SCADA alarms, smart meter last-gasp signals, and hyper-local weather data in real time, it determines the most probable cause (e.g., tree contact, animal, equipment failure) and calculates a dynamic Estimated Time to Restoration (ETR). This replaces manual dispatcher investigation and scripted call-center updates, directly reducing Customer Minutes of Interruption (CMI) and improving SAIDI/SAIFI scores through faster, more accurate intelligence.

Implementation integrates with the Outage Management System (OMS) and Customer Relationship Management (CRM) platform via APIs. The orchestration layer, built with frameworks like LangGraph, manages data retrieval, agentic reasoning, and approval gates. For governance, all cause classifications and ETRs are logged with confidence scores and routed to a human review queue for high-impact or low-confidence events before notification, ensuring auditability and control. Rollout is sequenced, starting with non-critical circuits.

This workflow automates the high-volume, repetitive task of post-outage diagnosis and communication. By correlating SCADA alarms, smart meter last-gasp signals, and hyper-local weather data in real time, a multi-agent system determines the most probable cause (e.g., tree contact, animal, equipment failure). This eliminates manual data aggregation by dispatchers, directly reducing the operational labor and time required to establish situational awareness, which is critical for managing customer expectations and field crew efficiency during widespread incidents.

Implementation integrates with the Outage Management System (OMS) and Customer Information System (CIS) via APIs. The architecture requires robust exception routing for low-confidence analyses or high-impact events, forcing a human-in-the-loop review before notification. Observability is built into each agent for audit trails and performance monitoring. The result is a closed-loop system that provides accurate customer communications within minutes of an event, directly lowering call center demand by managing customer inquiries proactively and improving reliability metrics.

This workflow automates the critical, time-sensitive bottleneck of diagnosing outage causes and managing customer communications. By correlating SCADA alarms, smart meter last-gasp signals, and hyper-local weather data in real time, it determines the most probable cause (e.g., tree contact, animal, equipment failure) and calculates a dynamic Estimated Time of Restoration (ETR). The operational upside comes from slashing manual dispatch and call center labor, while improving customer satisfaction through accurate, proactive notifications delivered via SMS or email, directly reducing Customer Minutes Interrupted (CMI).

Implementation requires integrating the orchestration layer (e.g., LangGraph) with the OMS, GIS, and weather APIs. The architecture must include validation gates where cause confidence scores below a threshold trigger a human review loop in the control room before notification. Observability is built into each agent for audit trails, and the system rolls out in phases, starting with high-reliability feeders to build operator trust before full deployment, ensuring governance over automated customer messaging during major events.

This workflow automates the critical, time-sensitive bottleneck of post-outage analysis and customer communication. By programmatically correlating SCADA alarms, smart meter last-gasp signals, and hyper-local weather feeds, it determines the most probable cause (e.g., tree contact, animal, equipment failure) and calculates a dynamic Estimated Time of Restoration (ETR). This eliminates manual data fusion by dispatchers, directly reducing the operational cost of incident investigation and the customer frustration of opaque, delayed updates.

Implementation integrates with the OMS, CIS, and customer communication platforms via APIs. The orchestrator, built on a framework like LangGraph, manages the data retrieval, agentic reasoning, and approval gates. A human-in-the-loop control is essential; the dispatcher can override the automated cause or ETR before notifications are sent. Observability is built in, logging all decisions, data sources, and actions for auditability and continuous model refinement, ensuring the system operates within strict utility governance frameworks.

When an outage occurs, the operational bottleneck is manual correlation. Dispatchers must manually sift through SCADA alarms, last-gasp signals from AMI, and external weather feeds to hypothesize a cause—a process that delays accurate customer communication and strains call centers. Automating this analysis provides immediate operational upside: it converts minutes of manual investigation into seconds of automated inference, allowing personalized SMS/email notifications with likely cause and a data-driven Estimated Time of Restoration (ETR) to be dispatched before the phones start ringing.

Implementation integrates with the Outage Management System (OMS) and CRM via APIs. The orchestration layer, built with a framework like LangGraph, sequences data ingestion, runs the classification model (which could be a rules engine or a lightweight ML model), and routes high-confidence determinations for automated notification. Low-confidence or complex cases are flagged for human dispatcher review. Controls include approval gates for ETR changes, audit logs for all cause attributions, and real-time monitoring of notification delivery rates to ensure system reliability during critical events.