AI Agentic Eligibility Optimization System
## The Solution A LangGraph-style DAG orchestration system employs six specialized AI agents using Monte Carlo simulation, ARIMA forecasting, and dynamic programming optimization to predict eligibility trajectories and recommend proactive interventions before coverage gaps occur..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Multi-Agent Orchestration System configuration showing enterprise architecture (DAG-based Parallel orchestration, 11-state FSM workflow engine, JSON-RPC 2.0 communication, Vector+KV hybrid memory, OpenTelemetry tracing), at-risk member selection with risk indicators, and hierarchical agent architecture diagram
AI Agent Execution dashboard displaying DAG workflow visualization at 83% completion with Supervisor, Data Validator, Pattern Analyzer, Monte Carlo, and Risk Scorer agents completed, Action Optimizer in reasoning phase, plus Agent Memory panel showing episodic and semantic memory utilization
Eligibility Analysis Results for member showing Executive Summary with 31 MEDIUM risk score, 28% lapse probability within 90 days, $585 potential savings vs COBRA, 45% projected ROI, and key findings including coverage lapse risk, employer concentration vulnerability, and optimal intervention window identification
Observability Console with enterprise telemetry showing System Health status for all components (Agent Orchestrator, Data Aggregator, ML Pipeline, Risk Engine, Cache Layer, Message Queue), Key Performance Indicators (1,248 req/s throughput, 183ms latency, 91.87% cache hit rate), and SLA Performance tracking
AI Agents
Specialized autonomous agents working in coordination
Supervisor Orchestrator Agent
Multi-agent workflows require coordinated execution, state management, and intelligent routing decisions that adapt to intermediate results.
Core Logic
The Supervisor Agent manages the DAG execution pipeline using an 11-state finite state machine. It initializes workflow context, routes tasks to specialist agents, manages parallel execution of independent analyses, aggregates results, and makes meta-level decisions about workflow progression based on intermediate confidence scores.
Data Validation Agent
Eligibility analysis requires clean, validated data, but member records often contain gaps, inconsistencies, or stale information that compromises prediction accuracy.
Core Logic
This agent performs comprehensive data quality assessment including schema validation, completeness checks, temporal consistency verification, and anomaly detection. It calculates data quality scores, identifies missing critical fields, and either enriches data from secondary sources or flags records requiring manual attention.
Pattern Analysis Agent
Work hour patterns in construction and trades follow complex seasonal and economic cycles that simple averaging cannot capture for accurate forecasting.
Core Logic
The Pattern Analyzer Agent applies ARIMA time-series analysis to identify trend components, seasonal patterns (12-month cycles), and autocorrelation structures in work history data. It detects regime changes, employer concentration risks, and trade-specific patterns to inform the forecasting model with historical volatility estimates.
Monte Carlo Simulation Agent
Point forecasts of future hour balances fail to capture uncertainty, leaving members unprepared for adverse scenarios that could cause coverage gaps.
Core Logic
This agent runs 10,000+ Monte Carlo simulation iterations using log-normal distributions calibrated to member-specific volatility. It generates probabilistic forecasts with 5th/50th/95th percentile trajectories, calculates coverage lapse probabilities, and identifies the most likely month for eligibility threshold breaches.
Risk Scoring Agent
Raw probability estimates don't account for member-specific risk factors like employer stability, trade outlook, or payment history that influence actual outcomes.
Core Logic
The Risk Scorer Agent applies logistic regression with L2 regularization across five risk factors: lapse probability, employer concentration, seasonal vulnerability, payment history, and trade economic outlook. It produces calibrated risk scores with confidence intervals and trend indicators for prioritized intervention targeting.
Action Optimization Agent
Members facing potential coverage gaps need personalized recommendations optimizing cost, timing, and likelihood of success—not generic advice.
Core Logic
This agent employs dynamic programming to solve the optimal intervention problem. It evaluates self-payment options, contribution catch-up strategies, and alternative coverage pathways. The optimizer considers member financial capacity, time until lapse, and intervention effectiveness probabilities to rank recommendations by expected utility.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization