Multi-Agent Automation of Vegetation Management Along Fiber Optic Routes

For telecom operators, vegetation management is a continuous operational cost center and a critical reliability risk. Manual patrols and reactive trimming are inefficient and fail to prevent outages caused by root intrusion on buried fiber or falling limbs on aerial lines. A custom multi-agent workflow automates this entire lifecycle, ingesting satellite, drone, and LiDAR data to detect encroachment, calculate risk based on species and growth rates, and generate prioritized work orders. This directly reduces field inspection labor by over 70% and prevents costly network downtime by scheduling pre-emptive clearance during planned maintenance windows.

Implementation requires integrating geospatial AI agents with network inventory systems like GE Smallworld and outside plant management tools such as 3-GIS. The orchestrator, built on LangGraph, manages data flows between detection, scoring, and scheduling agents, ensuring work orders in SAP or Maximo include precise GPS coordinates, required materials, and network impact assessments. Critical controls include human review gates for high-cost interventions, exception routing for ambiguous detections, and post-work drone verification to close the loop and retrain models, creating a self-improving system that quantifiably reduces operational risk and maintenance spend.

The workflow automates the labor-intensive cycle of manual patrols, data correlation, and schedule coordination that plagues telecom operators. It ingests satellite and drone imagery, processes it through specialized computer vision agents to detect encroaching vegetation, and automatically calculates risk scores based on proximity to aerial or buried fiber. This eliminates weeks of manual review, directly translating to lower survey costs and a quantifiable reduction in outage risk from root intrusion or line contact.

Implementation centers on a LangGraph or similar orchestration layer that sequences these specialized agents, each responsible for a discrete task like image analysis or scheduling. The system integrates directly with network inventory (OSS/BSS) and outside plant management tools via API agents, ensuring work orders reflect live maintenance windows. Critical controls include human review gates for high-cost interventions and automated compliance reporting to audit clearance activities against regulatory easement requirements, ensuring full operational governance.

Phase 1 establishes the core detection and data fusion pipeline. We integrate satellite and drone imagery feeds with your network inventory system (e.g., GE Smallworld, 3-GIS) to create a geospatially accurate asset register. AI agents are deployed to analyze this imagery, classifying vegetation species and calculating precise encroachment distances to both aerial and buried fiber routes. This phase delivers a centralized risk dashboard, automating the manual correlation of disparate data sources and providing the first layer of operational visibility and prioritization.

Phase 2 introduces full orchestration and closed-loop automation. The central orchestrator (built on LangGraph) routes high-confidence, low-cost clearance tasks directly to your CMMS (SAP, ServiceNow) for automated work order generation and mobile dispatch. High-risk or high-cost actions are routed through a human-in-the-loop approval gate. Phase 3 completes the loop with post-work drone verification, feeding performance data back to calibrate the risk models. This phased approach de-risks deployment, builds stakeholder trust, and sequences integration complexity, ensuring each stage delivers standalone value in reduced survey costs and outage prevention.

This workflow automates the detection of root intrusion risks for buried fiber and clearance needs for aerial lines, directly targeting the operational bottleneck of manual patrols and reactive repairs. The business case is built on preventing costly service outages and reducing vegetation management labor by 40-60%. Savings come from integrating satellite and drone imagery analysis with network inventory systems like GE Smallworld or 3-GIS, and outside plant management tools to schedule root pruning or line clearance within pre-defined maintenance windows, avoiding emergency dispatches.

Implementation requires a phased rollout, starting with a single high-risk corridor. Governance controls include human-in-the-loop approval gates for high-cost interventions, exception routing for ambiguous detections, and a full audit trail linking imagery to work orders. Monitoring is built on confidence scores from vision models and reconciliation of completed work against initial risk alerts. The architecture must handle data quality issues from cloud cover and integrate with legacy OSS/BSS systems, ensuring the workflow is both operationally resilient and financially justified.