Building supply chain multi-agent workloads in AWS GovCloud (US)

This article demonstrates how to build a multi-agent AI supply chain risk management system on AWS GovCloud using Amazon Bedrock, with agents that analyze inventory and disruption signals to rank items by readiness risk.

• Deploy three specialized agents (ETL, analytics, visualization) coordinated by a supervisor through Amazon Bedrock's Converse API
• Ingest inventory data and external disruption signals into Amazon S3, process via AWS Lambda/Batch, and expose through Amazon Athena
• Agents rank supply chain items by risk using weighted forecasting, disruption signals, and mission criticality
• Implement human approval gates for consequential actions; agents never place orders without analyst sign-off
• Use Amazon Bedrock Guardrails and inference profiles specific to AWS GovCloud (us-gov-west-1)
• Authenticate users via Amazon Cognito and deliver console through Application Load Balancer (CloudFront unavailable in GovCloud)
• Key GovCloud difference: Converse API requires inference profile ID, not on-demand model ID

The solution provides a reusable pattern for agentic AI workflows in AWS GovCloud that keep humans in control of critical decisions while automating cross-source data analysis and risk assessment.