Agentic System for Orchestrating Drone Inspection Follow-ups with Affected Customers

Manually identifying at-risk properties from drone footage and coordinating outreach is a high-latency, error-prone process for field supervisors. This workflow uses vision agents to parse inspection reports, a routing agent to match risks with customer records in the CIS, and a communication agent to generate and send personalized notifications via SMS/email. It automates the entire follow-up chain, freeing engineering and customer service staff for higher-value tasks.

Vegetation encroachment and asset defects identified by drones represent deferred maintenance and future outage risk. By automatically generating and delivering actionable service recommendations (e.g., 'Recommended tree trimming by our certified crew'), this workflow creates a qualified sales pipeline for utility vegetation management and repair services. It turns a cost center (inspection) into a proactive revenue and risk-mitigation channel.

Utilities face fines and liability for not addressing known hazards (e.g., trees near lines). This workflow creates an auditable trail: drone detection → customer notification → acknowledgment tracking → service scheduling. If a notified hazard later causes damage, the utility can demonstrate proactive duty of care. Automated compliance reporting ensures all high-risk detections are documented and escalated appropriately.

Without automation, inspection data sits siloed, and field crews are dispatched reactively. This workflow integrates with workforce management systems, automatically creating and prioritizing work orders based on risk severity and customer response. Crews receive optimized schedules with all relevant inspection imagery and customer consent pre-verified, reducing job-site delays and improving daily throughput.

Proactive, personalized communication about risks on a customer's property demonstrates care and technical competence. The AI agent manages the entire dialogue—from initial alert to scheduling confirmation and post-work follow-up—providing a seamless, transparent experience. This transforms a potential nuisance (utility work) into a valued service, directly improving CSAT and Net Promoter Scores (NPS).

The architecture is built for incremental value. A pilot can start by automating notifications for a single, high-risk circuit using a lightweight LangGraph orchestration layer that integrates with the CIS and a comms API. Success metrics (open rates, service uptake) are tracked automatically. Scaling involves adding more inspection types (e.g., corrosion, structure damage) and integrating with additional field systems like SAP ERP or Oracle WMS.

This agent ingests processed drone imagery (LiDAR, RGB) and inspection reports, overlays them with GIS parcel data and the Customer Information System (CIS) to identify properties with critical risks (e.g., vegetation within 10ft of a conductor, structural corrosion). It assigns a risk score and flags accounts for immediate notification, creating the target list for the workflow. Integration with platforms like Esri ArcGIS and DroneDeploy is typical.

An LLM-powered agent crafts context-aware messages using property-specific risk data, customer history, and regulatory templates. It generates explanations (e.g., 'A tree on your property poses a fire risk to line 1234'), recommended actions, and repair timelines. Notifications are formatted for SMS, email, and IVR and dispatched via an omnichannel communication platform (e.g., Twilio, SendGrid). Approval gates can be configured for high-risk or high-liability messages before sending.

This orchestration layer, often built with LangGraph or similar, manages inbound customer responses (opt-ins, questions, denials of access). It classifies intent, retrieves relevant case data, and triggers downstream actions: scheduling a trimming visit, creating a work order in the workforce management system, escalating complex queries to human agents, or updating the CIS with customer consent status. It maintains conversation state across channels.

A critical governance component that logs every agent decision, notification sent, customer response, and system interaction. It ensures defensible records for regulatory requirements (e.g., documentation of vegetation management notifications), supports customer dispute resolution, and provides a clear audit trail for liability protection. This layer integrates with enterprise logging platforms and generates compliance-ready reports automatically.

Upon customer approval, this component creates and dispatches a detailed work order to the appropriate field crew system (e.g., SAP ERP, Oracle WMS). It tracks completion status, and upon closure, triggers a final confirmation message to the customer and updates the master asset record in the GIS or Asset Management System. This closes the operational loop, turning inspection data into resolved risk.

A successful build requires phased deployment, starting with a pilot for a single risk type (e.g., vegetation). Key constraints include data quality of parcel maps, exception handling for unresponsive customers, and human-in-the-loop review for high-stakes escalations. The architecture must be designed for scalability to handle thousands of simultaneous inspections and integrate with legacy SCADA or OMS for holistic grid health management.