About This Architecture

Multi-agent AI infrastructure on AWS combining API Gateway, Lambda microservices, and EC2 inference instances to orchestrate intelligent workflows across Confluence, ServiceNow, and Axway SecureTransport. Ingress Lambda handles authentication and rate limiting, while specialized agents (Orchestrator, Knowledge, Log Analysis, Action) route requests to CPU and GPU compute tiers in isolated subnets. DynamoDB stores sessions and escalation records, S3 archives transfer logs and training data, and Secrets Manager secures credentials across the distributed system. This architecture demonstrates secure multi-VPC integration with external partners, compliance-ready audit logging via CloudWatch, and cost-optimized inference using always-on CPU and on-demand GPU instances. Fork and customize this diagram on Diagrams.so to adapt agent routing, add new Lambda functions, or adjust EC2 instance types for your workload. The design isolates training (offline GPU fine-tuning via Step Functions) from production inference, enforcing least-privilege access through NAT Gateway egress controls and KMS encryption across all data stores.

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How do I architect a multi-agent AI system on AWS that integrates with ServiceNow, Confluence, and secure file transfer platforms?

This diagram shows a production-grade AWS architecture using Lambda agents (Ingress, Orchestrator, Knowledge, Log Analysis, Action) to route requests to CPU and GPU inference tiers, with DynamoDB for session state, S3 for data archival, and Axway SecureTransport in a separate VPC for partner file transfers. Secrets Manager and KMS enforce encryption and least-privilege access across all components