AI Agentic Fleet Optimization System
Deploys a coordinated network of 5 specialized AI agents that work autonomously and collaboratively to optimize fleet operations in real-time. The multi-agent system provides full decision explainability through SHAP-based feature attribution, counterfactual analysis, and immutable audit trails for regulatory compliance.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Fleet Orchestrator Agent
Mining operations lack centralized intelligence to coordinate production strategy across multiple loading points, trucks, and dump destinations. Without orchestration, individual optimization decisions can conflict and create system-wide inefficiencies.
Core Logic
Serves as the master coordinator for all fleet operations, managing production strategy and equipment allocation. Analyzes zone queue depths, equipment availability, weather conditions, and operator fatigue indices to make allocation adjustments. Coordinates shift schedules to maintain minimum throughput levels and delegates specialized tasks to other agents while synthesizing their outputs into cohesive operational decisions.
Route Optimizer Agent
Truck assignments based on simple heuristics (nearest available) ignore critical factors like road conditions, queue times, payload matching, and fuel efficiency. This results in suboptimal cycle times and increased operational costs.
Core Logic
Performs real-time optimal path calculation considering 15+ variables including distance, estimated queue time, road conditions, fuel efficiency, and payload compatibility. Generates truck assignments with millisecond latency, providing rationale for each decision including why alternative routes were not selected. Continuously monitors road segment conditions and reroutes trucks when degradation is detected.
Equipment Health Monitor Agent
Equipment failures cause unplanned downtime costing thousands of dollars per hour. Reactive maintenance approaches cannot predict failures, and traditional scheduled maintenance often replaces components prematurely or too late.
Core Logic
Analyzes real-time IoT sensor data including engine temperature, transmission pressure, tire conditions, and vibration patterns. Uses machine learning models to identify failure patterns and calculate remaining useful life with confidence intervals. Issues proactive maintenance alerts, recommends load restrictions when anomalies are detected, and provides operational clearance after maintenance verification.
Demand Forecaster Agent
Operations cannot anticipate bottlenecks until they occur, resulting in cascading delays that compound throughout the shift. Lack of foresight prevents proactive resource reallocation.
Core Logic
Performs time-series prediction and pattern analysis to forecast production bottlenecks 30+ minutes before they occur. Analyzes inbound truck patterns, loading rates, and historical queue data to predict queue buildups. Provides throughput forecasts and demand surge predictions to enable proactive fleet rebalancing.
Bottleneck Resolver Agent
When bottlenecks are identified, supervisors must manually devise resolution strategies under time pressure. This delays response and often results in suboptimal interventions.
Core Logic
Automatically generates resolution strategies when bottlenecks are predicted or detected. Evaluates multiple resolution options including truck redistribution, backup resource activation, and load balancing. Provides expected outcomes for each strategy with cost savings estimates and risk assessments. Executes approved strategies through coordinated commands to affected assets.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization