AI Agentic Workflow for Automated Public Safety Power Shutoff (PSPS) Notifications

A custom PSPS notification workflow automates the critical path from wildfire risk detection to tiered customer communication, eliminating manual coordination delays that can compromise public safety and regulatory compliance. The system ingests real-time inputs from fire weather indices, SCADA, and GIS to execute pre-defined decision protocols, determining shutoff zones and required notification timelines. This directly reduces operational risk and the labor cost of manual call-outs during emergency events, while ensuring auditable adherence to PUC mandates for customer notice.

Implementation requires tight integration with the Customer Information System (CIS), geospatial platforms, and multi-channel communication gateways (e.g., Twilio, Amazon Pinpoint). The orchestrator, built with frameworks like LangGraph, manages state across the notification sequence, handles exception routing for failed deliveries, and provides a human-in-the-loop review gate for final shutoff confirmation. Observability is built in, with dashboards tracking delivery rates, customer acknowledgments, and time-to-notify metrics for post-event regulatory reporting.

The workflow automates a critical, regulated operational bottleneck: manually correlating wildfire risk indices with grid segments and orchestrating compliant customer notifications. Savings come from eliminating hours of manual data analysis and communication drafting, preventing regulatory fines, and reducing call-center surge during PSPS events. The system ingests real-time data from sources like the National Weather Service, SCADA, and GIS to execute decision protocols.

Implementation integrates with the Customer Information System (CIS) for customer data, Geographic Information Systems (GIS) for circuit mapping, and communication APIs. Controls include mandatory human-in-the-loop approval gates for final shutoff authorization, real-time monitoring dashboards, and immutable audit logs for regulatory reporting. The architecture is built using orchestration frameworks like LangGraph to manage agent state, decision logic, and exception routing to human operators.

This workflow automates the end-to-end PSPS protocol, from monitoring wildfire risk indices to executing tiered customer notifications. It eliminates the manual, error-prone process of cross-referencing weather data, grid models, and customer lists during high-pressure events. The operational upside comes from reducing notification latency from hours to minutes, ensuring regulatory compliance for critical care customers, and freeing operations staff to manage physical grid actions instead of communication logistics. The system integrates with SCADA, GIS, CIS, and external weather APIs.

Implementation requires an orchestrator, likely built with LangGraph, to manage state and conditional logic across agents for data retrieval, decision validation, and communication execution. A human-in-the-loop approval gate is mandatory before initiating a shutoff notification batch. The architecture must include real-time observability dashboards tracking notification delivery status, exception queues for failed communications, and immutable audit logs for regulatory review. Rollout sequencing should start with non-critical circuits to validate data integration and message accuracy before full deployment.

The rollout begins with a shadow-mode deployment, where the agentic system ingests real-time wildfire risk indices and executes decision protocols but all outbound customer notifications are held for human review and manual dispatch. This phase validates data accuracy, model logic, and integration stability with SCADA, GIS, and CIS systems without operational risk. Performance is measured against baseline manual processes for speed and decision consistency, establishing a benchmark for automation efficacy before live customer impact.

Governance is enforced through a centralized control plane that logs every agent decision, data source, and notification payload for audit. Approval gates are configured for specific thresholds, such as expanding the notification geofence or adjusting message templates for critical care customers. Post-rollout, a continuous monitoring layer tracks delivery rates, customer callback volumes, and regulatory adherence, feeding performance data back to retrain models and update protocols. This closed-loop system ensures the automated workflow remains compliant and effective as wildfire risks and regulatory frameworks evolve.