Multi-Agent Customer Retention & Service Intelligence Platform
This digital worker deploys 9 specialized AI agents coordinated by a Mission Commander in a supervisor pattern. The system supports multiple mission types (customer retention, service optimization, inventory planning, revenue maximization, EV transition analysis) with autonomous execution capabilities, real-time alerts, adaptive learning, and human-in-the-loop controls for high-impact decisions.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Configuration - Mission type selection (Customer Retention, EV Transition, Predictive Maintenance), supervisor architecture, and customer records input
Agent Network Orchestration - Live activity stream with multi-agent collaboration, tool executions, and mission phase tracking
AI Mission Results - Retention actions for at-risk customers with churn probability reduction, revenue protected, and executive summary
System Observability - Agent performance metrics, system logs, resource utilization, and mission timeline tracking
AI Agents
Specialized autonomous agents working in coordination
Orchestration & Strategy Supervisor
Coordinating complex multi-agent missions, delegating tasks to specialist agents, monitoring progress, resolving conflicts between agent recommendations, and ensuring overall mission objectives are achieved.
Core Logic
The Mission Commander serves as the supervisor agent with capabilities for task_delegation, progress_monitoring, conflict_resolution, final_approval, autonomous_decision, and adaptive_learning. It coordinates all specialist agents, manages the workflow through 7 execution phases, handles task delegation and progress monitoring, performs conflict resolution when agents have differing recommendations, provides final approval authority, and enables autonomous decision-making for high-confidence scenarios.
Customer Analytics & Risk Assessment Specialist
Identifying customers at risk of churning before they leave, understanding customer lifetime value, segmenting customers for targeted campaigns, and analyzing customer behavior patterns.
Core Logic
Analyzes customer data with capabilities for churn_prediction, clv_calculation, segmentation, behavior_analysis, sentiment_analysis, and journey_mapping using tools including DMS Customer API, Service History Query, Churn Prediction Model (XGBoost), and CLV Calculator. Uses XGBoost models with features including recency, frequency, monetary value, satisfaction scores, and tenure. Calculates probabilistic CLV, performs customer segmentation, conducts sentiment analysis, and maps customer journeys to identify intervention opportunities.
Service & Scheduling Intelligence Specialist
Maximizing service department capacity utilization, optimizing technician scheduling, matching the right technician to each job, and determining optimal pricing for retention offers.
Core Logic
Analyzes service capacity with capabilities for service_recommendation, schedule_optimization, resource_allocation, pricing, and technician_matching using Schedule Optimizer (FFD) and Service Pricing Engine tools. Uses First Fit Decreasing scheduling algorithms, calculates current utilization and available appointment slots, identifies peak availability windows, and determines optimal retention pricing using elasticity-based models that maintain target margins while maximizing conversion probability.
Retention & Engagement Planning Specialist
Developing effective personalized retention strategies, designing targeted campaigns, optimizing offer values, selecting the right communication channels, and A/B testing campaign variations.
Core Logic
Develops tiered retention strategies with capabilities for retention_planning, campaign_design, offer_optimization, channel_selection, and ab_testing using Campaign Optimizer and Offer Generation Engine tools. Tier 1 (Critical risk, High CLV) receives personal calls and VIP offers, Tier 2 (High risk, Medium CLV) receives SMS and targeted discounts, Tier 3 (Medium risk) enters automated email sequences. Uses ML-based campaign optimization to maximize retention ROI within budget constraints.
Parts & Inventory Forecasting Specialist
Forecasting parts demand to prevent stockouts, optimizing inventory levels to reduce carrying costs, planning reorders based on lead times, and analyzing supplier reliability.
Core Logic
Generates demand forecasts with capabilities for demand_forecasting, inventory_optimization, reorder_planning, supplier_analysis, and supply_chain_risk using Demand Forecaster (Holt-Winters) and Inventory Management System tools. Uses Holt-Winters triple exponential smoothing with seasonal adjustment, identifies items below reorder point, flags critical items requiring expedited ordering, calculates optimal safety stock levels, and assesses supply chain risks.
Validation & Compliance Specialist
Ensuring recommendation quality and consistency, validating data accuracy, checking regulatory compliance (GDPR, CCPA, CAN-SPAM), and maintaining audit trails for governance.
Core Logic
Validates all recommendations with capabilities for recommendation_validation, compliance_check, data_quality, risk_assessment, and audit_trail using Data Quality Validator and Compliance Engine tools. Checks against quality thresholds covering completeness, accuracy, consistency, and timeliness. Performs pre-campaign compliance checks against relevant regulations, adjusts offers for margin compliance, generates overall confidence scores, and maintains detailed audit trails.
Electric Vehicle Intelligence Agent
Understanding the EV market transition impact on dealership operations, identifying customers ready for EV adoption, assessing charging infrastructure requirements, and planning service department evolution.
Core Logic
Analyzes EV market trends with capabilities for ev_market_analysis, customer_ev_readiness, charging_infrastructure, ev_inventory_planning, and transition_roadmap using EV Market Intelligence tools. Uses ML models to score customer EV readiness based on driving patterns and preferences, assesses local charging infrastructure density, develops EV inventory planning strategies, and creates transition roadmaps for service department capabilities including technician training needs.
Vehicle Health & Maintenance AI Specialist
Predicting vehicle maintenance needs before failures occur, identifying potential warranty claims, managing recall notifications, and enabling proactive service outreach.
Core Logic
Uses LSTM neural networks with capabilities for failure_prediction, warranty_analysis, recall_management, proactive_service, and parts_lifecycle using Vehicle Health Predictor tools. Trained on mileage, service history, driving patterns, and vehicle age to predict upcoming maintenance needs. Forecasts warranty claims, manages recall status, generates proactive service recommendations, and predicts parts demand based on predicted maintenance activities.
Omnichannel Experience Design Specialist
Creating cohesive customer experiences across all touchpoints (email, SMS, app, in-person), personalizing interactions based on customer preferences, predicting NPS scores, and managing loyalty programs.
Core Logic
Designs personalized customer journeys with capabilities for journey_orchestration, touchpoint_optimization, personalization, nps_prediction, and loyalty_management using Journey Orchestration Engine tools. Uses ML-based orchestration across all channels, optimizes touchpoint interactions, implements personalization engines, predicts NPS trends, manages loyalty tier progressions, and generates segment-specific journey maps with conversion rate projections.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization