AI Agentic Workflow for Wildfire Risk Mapping and Preemptive Crew Dispatch

This workflow automates the critical bottleneck of correlating disparate, high-volume data streams—satellite vegetation indices, weather forecasts, historical fire perimeters, and asset GIS data—into a single, actionable risk heatmap. The operational upside is direct: by pre-positioning crews and equipment in high-probability zones, utilities reduce catastrophic outage risk, avoid costly PSPS events, and improve capital efficiency through targeted vegetation management. Savings come from avoided regulatory fines, reduced emergency mobilization costs, and extended asset life.

Implementation integrates a central orchestrator, built on frameworks like LangGraph, to manage specialized agents for data fusion, model scoring, and system integration. The architecture must include approval gates for high-cost dispatches, real-time observability dashboards, and a feedback loop where post-work drone verification data retrains the risk models. Key constraints are data latency from satellite providers, the need for human-in-the-loop validation for ambiguous zones, and seamless integration with legacy CMMS and field force management systems to ensure work orders are executed without manual re-entry.

The operational bottleneck is the manual correlation of disparate, high-velocity data streams—satellite vegetation indices (NDVI), NOAA weather forecasts, historical ignition data, and asset GIS layers—into a single, actionable risk score. Automating this fusion eliminates a 6-8 hour daily analytical delay, allowing risk-based crew positioning 24-48 hours ahead of critical fire conditions. The business upside is direct: reduced catastrophic asset loss, lower PSPS customer impact, and optimized overtime spend by dispatching crews preemptively rather than reactively.

Implementation centers on a LangGraph-based orchestrator that sequences specialized agents for data validation, model execution, and system integration. The Geospatial Modeling Agent runs in a containerized environment, scoring pixels against a calibrated risk model. The output heatmap triggers the Dispatch Engine, which integrates with SAP, IBM Maximo, or a field service management (FSM) platform via API agents to generate work orders and adjust crew schedules. Critical controls include a human-in-the-loop review gate for high-consequence dispatches and full observability logging of each agent's decision rationale for audit and model refinement.

Phase 1 establishes the data ingestion and fusion backbone, connecting satellite feeds (e.g., Sentinel-2 for NDVI), weather APIs, and asset GIS into a unified geospatial data lake. This initial build focuses on data quality controls and exception routing, delivering a validated, high-fidelity risk input layer. The goal is to prove data reliability and pipeline resilience before introducing autonomous decision logic, ensuring the foundation supports mission-critical alerts.

Phase 2 deploys the LangGraph-based risk modeling agent, which synthesizes the fused data into dynamic heatmaps and generates preliminary dispatch recommendations. This stage incorporates a mandatory human review gate within the orchestrator for all high-consequence actions, allowing operators to build trust in the AI's outputs. Finally, Phase 3 integrates the approved recommendations directly into SAP or ServiceNow for work order generation and crew dispatch, closing the loop from prediction to field action with full auditability.

Governance begins with a formal risk-scoring policy that defines acceptable confidence thresholds for automated dispatch. High-risk, high-confidence zones may trigger pre-staging of crews and equipment automatically, while medium-risk or ambiguous findings are routed to a human dispatcher for review within a centralized dashboard like ServiceNow or a custom command center. This control layer prevents over-reaction to false positives and ensures accountability for all resource movements, creating a defensible audit trail for regulators and internal safety reviews.

Phased rollout is critical for managing scale and complexity. Phase 1 targets a single high-risk circuit, integrating the risk model with the GIS and a test instance of the field force management system (e.g., SAP Fieldglass). Phase 2 expands to a full region, adding the automated approval gates and integrating live weather feeds. The final phase deploys the system across the entire service territory, with continuous monitoring of model drift, dispatcher override rates, and incident response times. This measured approach de-risks implementation and allows for tuning of agent logic and human-AI collaboration before full-scale automation.