Enterprise Automotive Emergency Resolution System
Deploys a 6-agent orchestrated AI system with real-time coordination, chain-of-thought reasoning, RAG-powered knowledge retrieval, and human-in-the-loop escalation gates to deliver end-to-end emergency resolution within 58 minutes average..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Multi-agent orchestration dashboard showing emergency resolution workflow initialization with 6 specialized AI agents including Diagnostic, Parts Intelligence, Service Coordinator, and Dispatch agents with circuit breaker patterns and real-time progress tracking
LLM reasoning and RAG retrieval interface demonstrating chain-of-thought diagnostic analysis with GPT-4 Turbo and Claude 3 Opus, semantic search results from technical service bulletins, and 94% confidence Fuel Pump Relay Failure diagnosis
Resolution plan output showing customer-friendly executive summary explaining the fuel pump relay failure, detailed diagnosis with 94% confidence, parts requirements with availability status, and total cost breakdown of $196.35
AI predictive analytics dashboard displaying ML-powered metrics including 96.8% resolution success rate, 2.4hr average resolution time, EUR 847 estimated cost, 4.7/5.0 customer satisfaction, and risk assessment using XGBoost Ensemble + LSTM model
AI Agents
Specialized autonomous agents working in coordination
Diagnostic Agent
Accurately diagnosing vehicle faults from ambiguous DTC codes and sensor data requires expert interpretation. Traditional diagnostics miss correlations between symptoms, leading to misdiagnosis and repeated service visits.
Core Logic
Employs Bayesian-inspired multi-factor confidence calculation analyzing DTC codes (40% weight), sensor correlations (30%), historical patterns (20%), and component lifecycle data (10%). Generates differential diagnoses with probability scoring using real OBD-II codes (SAE J2012 standard). Tools: DTC_CODE_ANALYZER, SENSOR_READER, PATTERN_MATCHER. Complexity: O(n) with 2-5ms execution.
Parts Intelligence Agent
Sourcing replacement parts across a network of 340+ dealers involves manual inventory checks, delivery time estimation, and cost comparison, causing delays in emergency situations.
Core Logic
Implements multi-dealer scoring optimization with weighted factors: stock availability (40%), distance (35%), quantity (15%), supplier reliability (10%). Calculates delivery ETA using formula: `time = (distance / courier_speed) ร 60 + prep_time`. Handles backorder scenarios with 48h+ delay handling. Tools: PARTS_INVENTORY_LOOKUP, DELIVERY_CALCULATOR, COST_ESTIMATOR. Complexity: O(n log n), <20ms typical.
Service Center Selection Agent
Matching stranded vehicles to optimal service facilities requires evaluating multiple criteria including parts availability, technician expertise, workload, and customer historyโimpossible to optimize manually under time pressure.
Core Logic
Applies AHP-inspired Multi-Criteria Decision Analysis (MCDA) with 6-criteria weighted scoring: parts availability (30%), distance (20%), technician expertise (20%), slot availability (15%), customer history (10%), rating (5%). Technician scoring: `tech_score = (rating/5) ร (min(exp, 15)/15)`. Tools: SERVICE_CENTER_QUERY, TECHNICIAN_LOOKUP, WORKLOAD_ANALYZER. Complexity: O(n ร m), <10ms.
Dispatch Optimization Agent
Calculating accurate ETAs for roadside assistance requires real-time traffic analysis, distance computation, and provider availability assessment across multiple variables.
Core Logic
Uses Haversine formula for great-circle distance calculation: `distance = R ร 2รatan2(โa, โ(1โa))`. Applies traffic-adjusted ETA with rush hour multipliers (7-9am, 4-7pm at 1.6x) and urban road network factor (1.3x). Provider scoring: `score = ETA_score ร 0.60 + rating_score ร 0.25 + exp_score ร 0.15`. Tools: ROUTE_CALCULATOR, TRAFFIC_ANALYZER, ETA_PREDICTOR. Complexity: O(n log n), <15ms.
Customer Communications Agent
Communicating technical automotive issues to non-technical customers requires context-aware messaging that reduces anxiety while providing accurate information across multiple channels.
Core Logic
Generates sentiment-aware messages calibrated to customer technical literacy (0-10 scale) with Flesch-Kincaid readability optimization. Produces 3 channel variants: SMS (160 char), Email (comprehensive), App (brief). Applies anxiety-reduction messaging with confidence-dependent reassurance levels. Tools: MESSAGE_GENERATOR, SENTIMENT_ANALYZER, CHANNEL_FORMATTER. Complexity: O(n), <3ms.
Quality Assurance Agent
Resolution plans may contain cost outliers, incompatible parts, unrealistic timelines, or non-compliant recommendations that erode customer trust and increase operational costs.
Core Logic
Performs multi-layer statistical validation using Z-score analysis for cost outlier detection: `Z = (X - ฮผ) / ฯ`. Validates 5 layers: confidence threshold (โฅ70%), parts compatibility, service capability, cost reasonableness, timeline feasibility. Uses Beta distribution for confidence: `confidence = (ฮฑ / (ฮฑ + ฮฒ)) ร 100`. Pass threshold: 4 of 5 checks. Tools: COST_VALIDATOR, COMPATIBILITY_CHECKER, TIMELINE_ANALYZER. Complexity: O(n), <5ms.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization