Traditional employee benefits administration systems rely on scheduled batch processes running on dedicated infrastructure for eligibility verification, enrollment processing, and carrier file submissions, incurring continuous operational costs regardless of actual enrollment volumes. This paper presents an AI-driven, agent-based framework for real-time benefits administration using managed container services (MCS) integrated with machine learning for intelligent eligibility determination, document verification, and compliance validation. Through empirical analysis of 72 enterprise implementations processing 5,000-250,000 monthly enrollment events, the study demonstrates that hybrid MCS architectures combined with AI agents achieve 73% cost reduction compared to traditional approaches while improving enrollment accuracy by 97% and reducing compliance violations by 94%. A quantitative decision framework is introduced, incorporating enrollment volume, plan complexity, and AI agent selection to optimize container resource allocation and ML service integration. Comparative analysis across AWS ECS Fargate with Amazon Rekognition and Comprehend Medical, Azure Container Instances with Azure AI Document Intelligence, and Google Cloud Run with Vertex AI reveals platform-specific performance and accuracy trade-offs. Real-world validation with a mid-market employer processing 85,000 annual enrollment events demonstrates a monthly infrastructure cost reduction from $6,842 to $1,842 while achieving 99.4% enrollment accuracy and zero HIPAA/ERISA compliance violations over a 180-day period [1][6][7].

Managed Container Service Benefits Administration Machine Learning AI Agents Document Intelligence Eligibility Verification HIPAA Compliance Event-Driven Architecture