Autonomous Multi-Agent Fleet Management & Optimization Platform v3.0
Deploys 9 AI agents collaborating through autonomous decision-making, agent negotiation, and continuous learning across 12 phases using Weibull prediction, constraint satisfaction scheduling, and ESG analysis..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Fleet Intelligence Configuration - Analysis type selection (Full Analysis, Maintenance, Cost Analysis, Risk Assessment) with 6 AI agents, 7 analysis tools, configurable time horizons, and optimization priority settings
Schedule Optimization Workflow - 12-phase process at 38% completion showing Fleet Intelligence Orchestrator and Data Collection Agent progress with system resource utilization metrics
Report Generation Phase - Active workflow monitoring with 9 AI agents, tool execution panel showing fleet database queries, and live metrics including token usage, vehicle counts, and latency distribution
Fleet Intelligence Final Report - Analysis complete with critical issues alert, fleet health score (74.3/100), €47,320 savings identified, 5 vehicles needing attention, and detailed 12-phase process flow
AI Agents
Specialized autonomous agents working in coordination
Central Workflow Coordinator & Conflict Resolution Manager
Fleet analysis requires coordinating agents across 12 phases, resolving conflicts between cost vs. safety vs. compliance objectives, and ensuring output quality.
Core Logic
Powered by Claude Sonnet 4, serves as central coordinator creating execution plans across 12 phases, distributing tasks to 8 agents, monitoring with real-time updates, and resolving conflicts through negotiation protocols. Implements self-healing for agent failures, maintains correlation IDs, and generates metrics (duration, tokens, tool calls). Final QA validates consistency.
Telemetry Aggregation & IoT Integration Specialist
Analysis requires data from vehicle databases, OBD-II telemetry, service history, IoT sensors, and GPS tracking. Incomplete or stale data causes inaccurate predictions.
Core Logic
Powered by Claude Haiku 3.5 for efficiency, systematically gathers fleet data: vehicle specs, service history, maintenance records. OBD-II retrieval captures engine temperature, oil pressure, brake wear, battery voltage, and DTCs. Prioritizes vehicles with health scores <70%. Validates quality with completeness scoring and anomaly flagging.
ML-Powered Failure Prediction & Risk Scoring Specialist
Unexpected breakdowns cost €2,400+ per incident with safety risks. Traditional mileage/time schedules ignore actual component condition and usage patterns.
Core Logic
Powered by Claude Sonnet 4, applies Weibull distribution models predicting failure probability from wear patterns, usage, and history. Calculates composite risk using 5 factors: Component Wear (30%), Usage Patterns (25%), Maintenance Compliance (20%), Age & Mileage (15%), Environmental (10%). Identifies critical findings, generates risk profiles, and quantifies financial exposure with 89%+ accuracy.
Constraint-Based Maintenance Schedule Optimization Specialist
Scheduling must balance technician availability, bay capacity, parts availability, vehicle criticality, and business hours. Manual scheduling misses bundling opportunities.
Core Logic
Powered by Claude Sonnet 4, applies constraint satisfaction algorithms considering: bay capacity (3 bays), technician hours (40/week), business hours (08:00-18:00), preferred days, and parts availability. Identifies bundling opportunities for same or co-located vehicles. Resolves conflicts with priority allocation. Outputs complete schedule with bays, technicians, durations, and savings.
TCO Analysis & Savings Identification Specialist
Fleet costs are often poorly understood. Hidden costs in reactive repairs, missed warranties, suboptimal contracts, and inefficient scheduling erode profitability.
Core Logic
Powered by Claude Sonnet 4, performs TCO calculations with NPV analysis, identifies high-cost vehicles, and finds savings: preventive vs. reactive (50%+ savings), service bundling, warranty recovery (40-60% underutilized), vendor optimization. Generates 12-month forecasts with optimized projections, tracks cost drivers, and calculates investment ROI.
Multi-Audience Report Synthesis & Visualization Specialist
Complex analysis must be communicated to different stakeholders: executives need summaries, technicians need specs, analysts need financials. Generic reports fail all audiences.
Core Logic
Powered by Claude Sonnet 4, synthesizes results for 4 audiences: Executive (KPIs, key findings, strategic recommendations), Technical (methodology, model performance, data quality), Financial (ROI, cost projections, investment analysis), Operational (schedules, service details, resources). Generates 12+ visualizations including health distributions, risk heatmaps, and cost breakdowns.
Self-Directed Decision Making & Emergency Response Specialist
Fleet operations require rapid 24/7 decisions on prioritization, resource allocation, and emergencies. Human delays escalate costs and risks. Conflicting agent recommendations need resolution.
Core Logic
Powered by Claude Opus 4 for advanced reasoning, makes autonomous decisions within configurable authority levels. Resolves conflicts (safety vs. cost vs. compliance) using trade-off analysis. Decisions include type, confidence (0-100), reasoning chain, alternatives with pros/cons, and impact. High-confidence (>85%) proceeds automatically; others escalate for approval.
Pattern Recognition & Continuous Improvement Specialist
Fleet data contains valuable patterns for improving predictions. Without systematic learning, insights are lost and prediction models become stale.
Core Logic
Powered by Claude Sonnet 4, analyzes historical data for patterns, anomalies, prediction improvements, and cost trends. Maintains knowledge base with confidence scores, validation status, and effectiveness tracking. Capabilities: correlation analysis (e.g., fast charging impact on EV batteries), model refinement, seasonal patterns, and best practice extraction.
Regulatory Compliance & Carbon Footprint Optimization Specialist
Fleet operators face complex regulations (EU emissions, safety, inspections) with penalties. ESG requirements demand carbon tracking and reduction. Manual monitoring is error-prone.
Core Logic
Powered by Claude Sonnet 4, provides dual-function analysis. Compliance: scoring, vehicle status, violation tracking, upcoming regulation assessment (EU 2025/2030, Euro 7), certifications, audit readiness. Sustainability: carbon footprint (CO2e/year), emissions per vehicle, ESG rating (A-F), reduction opportunities with costs/payback, offset options, and trajectory vs. targets.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
10 technologies
Architecture Diagram
System flow visualization