AI Agentic Predictive Maintenance System
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Illustrative preview (sample data, not verified outcomes): Mission Control dashboard with workflow configuration, portfolio scope selection, priority levels, and agent fleet status monitoring
Illustrative preview (sample data, not verified outcomes): Agent Orchestration Engine displaying 11-agent network topology with real-time workflow phases and tool invocation tracking
Illustrative preview (sample data, not verified outcomes): Human-in-the-Loop review interface showing AI maintenance recommendations with failure predictions, cost analysis, and ROI metrics
Illustrative preview (sample data, not verified outcomes): Executive Summary Results with operational savings, work orders scheduled, prevented failures, and sustainability impact metrics
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator
Complex predictive maintenance workflows require coordination of data collection, ML prediction, risk assessment, scheduling, and autonomous decision-making across 11 specialist agents.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Initializes and coordinates the 9-phase agentic workflow, manages inter-agent communication via message passing, tracks progress and token usage, handles human approval checkpoints, and aggregates final workflow output with comprehensive metrics.
Data Collection Agent
Predictive maintenance requires aggregating sensor data from millions of IoT devices across thousands of sites with varying data quality and connectivity.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Queries sensor databases, streams real-time IoT telemetry from 847M+ sensors, detects anomalies in data patterns, validates data quality metrics, and prepares normalized datasets for downstream ML prediction agents.
Failure Prediction Agent
Predicting equipment failures requires sophisticated ML models that can handle diverse equipment types, failure modes, and operating conditions.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Runs ensemble ML analysis combining Weibull distribution for reliability modeling, XGBoost for gradient boosting prediction, and Transformer models for sequence pattern recognition. Performance requires validation on representative workloads.
Risk Assessment Agent
Equipment failures impact multiple tenants (Vodafone, O2, Three, EE) with varying SLA tiers (Gold, Silver, Bronze) and penalty structures requiring portfolio-level risk quantification.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Queries SLA databases for all operators, calculates breach probability using multi-factor analysis, quantifies financial exposure (up to £9,100/hour for quad-tenant Gold sites), and generates prioritized mitigation actions based on risk-adjusted value.
Schedule Optimization Agent
Coordinating maintenance across thousands of sites requires optimizing technician assignments considering skills, certifications, travel time, parts availability, and work priority.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Analyzes technician availability and skills matrix (Power Systems, Cooling, Antenna, Tower Climbing certifications), runs Vehicle Routing Problem optimization combined with genetic algorithms, verifies parts inventory, and generates optimized schedules with 96%+ efficiency scores.
Memory & Learning Agent
ML models improve over time but require systematic collection of outcomes, similar case retrieval, and continuous model refinement.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Retrieves similar historical failure cases with configurable confidence thresholds (85%+), updates prediction models based on actual outcomes, tracks first-time-fix-rate improvements, and maintains institutional knowledge for pattern recognition.
5G Network Intelligence Agent
5G network health affects overall site performance and requires specialized monitoring of latency, throughput, network slices, and spectrum efficiency.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Autonomously monitors 8,400+ 5G-enabled sites, tracks average latency (targeting 4.2ms), peak throughput (2.1 Gbps), active network slices (847+), mmWave and sub-6GHz coverage metrics, eMBB sessions, mMTC devices, and uRLLC services with real-time alerting.
Sustainability & Carbon Agent
Telecommunications infrastructure contributes to carbon emissions and tower companies must track progress toward Net Zero commitments.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Calculates carbon footprint reduction (2,800+ tonnes/year), tracks energy savings (1.24M kWh), monitors renewable energy percentage (67%+), diesel generator runtime, solar panel output, battery efficiency, carbon credits earned, and generates green maintenance scores.
Weather Intelligence Agent
Weather conditions impact both maintenance safety (technician access) and equipment performance (battery degradation, antenna misalignment, heating/cooling loads).
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Fetches Met Office forecasts for 14-day planning horizon, analyzes storm risk across all sites, correlates weather patterns with maintenance schedules, identifies sites at risk (high wind, cold snap, lightning), and recommends postponements or preventive actions.
Edge Computing Monitor
Edge computing infrastructure at tower sites requires specialized health monitoring to ensure low-latency services and AI inference capabilities.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Scans 3,500+ edge nodes across all regions, monitors average edge latency (2.4ms), tracks processing capacity utilization, AI inference operations (45K+/day), data processed locally (78%), MEC application health, and edge security scores.
Autonomous Dispatch Agent
Critical equipment failures require immediate technician dispatch without waiting for human approval to prevent SLA breaches.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Makes autonomous dispatch decisions for issues exceeding critical urgency thresholds, pre-positions spare parts based on failure predictions, alerts on-call teams, and tracks autonomous actions for audit compliance while maintaining human oversight for non-critical decisions.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization