Multi-Agent Eligibility Intelligence Digital Worker
Orchestrates 10 specialized AI agents using A2A (Agent-to-Agent) protocol for collaborative intelligence. Collects data from multiple employers, detects fraud patterns, ensures regulatory compliance, analyzes market conditions, runs Monte Carlo simulations for scenario planning, and generates personalized recommendations with consensus voting across agents.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Eligibility Intelligence - Multi-agent A2A collaboration workflow
Eligibility Intelligence - Data collection and fraud detection
Eligibility Intelligence - Scenario simulation and predictions
Eligibility Intelligence - Recommendations and validation
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator
Complex eligibility assessments require coordination across multiple specialized agents, state tracking, and workflow management using modern AI collaboration patterns.
Core Logic
Coordinates 10-agent workflow using A2A protocol, manages execution state, dispatches tasks to specialist agents in optimal sequence, tracks progress, handles retries with exponential backoff, aggregates results from all agents, and facilitates consensus building for final recommendations.
Data Collection Agent
Member eligibility data is fragmented across multiple employer systems, databases, and APIs. Manual data gathering is slow and incomplete.
Core Logic
Queries member database and employer hour submission APIs. Aggregates work hours from all employers (handles multi-employer scenarios), retrieves dependent information including special needs flags, identifies pending and late employer reports, validates data quality, and streams collected data to downstream agents.
Fraud Detection Agent
Eligibility fraud through falsified hours, employer collusion, and identity manipulation costs funds millions. Pattern detection across employers and members is beyond human capability.
Core Logic
Runs ML fraud detection model against 50,000+ historical fraud cases. Analyzes employer hour reporting patterns for inconsistencies, performs social network analysis to detect fraud rings, checks for suspicious employer-member relationships, calculates fraud risk score (0-100), and clears members for standard or enhanced processing.
Compliance Auditor
Eligibility decisions must comply with ACA Section 4980H employer mandates, HIPAA privacy rules for PHI access, ERISA fiduciary duties, COBRA regulations, and multi-state insurance laws.
Core Logic
Validates against ACA employer shared responsibility requirements, ensures HIPAA minimum necessary standard for data access, audits ERISA prudent expert rule compliance for dependent coverage decisions, checks state-specific regulations (CA, NV, AZ), maintains complete audit trail, and generates compliance score (0-100).
Market Analysis Agent
Eligibility recommendations should account for real-world economic factors affecting member work hours and healthcare costs. Static analysis misses market dynamics.
Core Logic
Fetches real-time economic indicators from BLS (unemployment, construction employment, wage growth). Analyzes healthcare cost inflation trends (medical CPI, COBRA costs), retrieves weather risk data affecting outdoor construction work, evaluates industry sector outlooks, and provides market context for work hour recovery probability.
Analysis Agent
Raw data requires deep analysis to identify coverage risk patterns, employer reliability issues, and factors contributing to eligibility gaps.
Core Logic
Performs pattern recognition across employer hour histories, calculates coverage gap percentages, identifies risk factors (seasonal work patterns, employer reliability, dependent vulnerabilities), detects anomalies in reporting patterns, correlates multiple risk indicators, and streams analysis insights to other agents.
Predictive Agent
Members need accurate forecasts of whether they will meet eligibility requirements. Static calculations don't account for trends and seasonality.
Core Logic
Runs LSTM model with 6-month lookback for hours prediction. Accounts for seasonal construction work patterns, employer-specific reporting reliability, calculates coverage gap probability, generates risk scores with confidence intervals, predicts month-end hours with 87%+ accuracy.
Scenario Simulator
Members facing coverage gaps need to understand outcomes of different intervention strategies (self-pay, work more hours, combination approaches).
Core Logic
Runs Monte Carlo simulation with 10,000 iterations across 4 scenario types (no action, self-pay, work more, hybrid). Calculates success probability, expected cost, and risk level for each scenario. Performs sensitivity analysis to identify most impactful factors, projects timelines to coverage recovery, and recommends optimal strategy based on risk-adjusted outcomes.
Recommendation Agent
Eligibility assessment findings must be synthesized into actionable, prioritized recommendations that balance cost, risk, and coverage certainty.
Core Logic
Evaluates intervention options (self-pay enrollment, additional work requests, employer follow-up). Performs cost-benefit analysis for each option, considers member-specific factors (pregnant spouse, special needs dependents), builds consensus with other agents via A2A protocol, generates prioritized recommendations with confidence scores and reasoning.
Validation Agent
AI recommendations must be validated against eligibility policies, guardrails must prevent hallucination, and compliance requirements must be verified before delivery.
Core Logic
Validates recommendations against fund eligibility policy documents. Runs guardrail checks (PII detection, hallucination prevention, policy compliance), verifies data accuracy by cross-referencing employer submissions, ensures all recommendations are defensible and documented, clears output for member delivery.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
11 technologies
Architecture Diagram
System flow visualization