Multi-Agent Multimodal Data Analysis on AWS – Part 2: Multi-Agent Orchestration and Predictive Analytics

This article demonstrates building a multi-agent AI system on AWS for healthcare data analysis, using specialized agents for clinical, genomic, and imaging data orchestrated by a supervisor agent with predictive analytics capabilities.

• Build specialized agents with Amazon Bedrock AgentCore and Strands SDK for clinical trials, PubMed, clinical data, genomics, and imaging modalities
• Supervisor agent interprets user intent and routes queries to appropriate specialized agents, synthesizing outputs across data silos
• Connect agents to healthcare data stores via Model Context Protocol (MCP) servers: HealthLake, HealthImaging, S3 Tables, and external APIs
• Deploy agents on Amazon Bedrock AgentCore with Cognito authentication, automatic scaling, and integrated observability
• Add model inference agent serving Amazon SageMaker AI predictions for disease risk using multimodal clinical, imaging, and genomic features
• React-based web UI on AWS Amplify provides conversational interface with real-time agent reasoning visualization

The architecture enables natural-language querying across healthcare data modalities, cross-modal synthesis, and disease risk prediction within a governed, scalable framework.