Architecting distributed agentic AI workloads across AWS hybrid cloud services
Global Infrastructure and Sustainability Blog
This article presents architectural patterns for deploying distributed agentic AI workloads across AWS hybrid cloud services, addressing data residency and compliance requirements.
- Local agents using Strands framework run foundation models on AWS Outposts and Local Zones without data leaving the boundary
- Decompose workloads by placing GPU-intensive inference on G-series instances and CPU-bound agent orchestration on general-purpose instances
- Distributed agents use Amazon Bedrock AgentCore as a Region-based orchestrator coordinating edge agents via A2A protocol or MCP
- AgentCore Gateway federates access to geographically distributed tools and data sources from a single Region-based agent
- Stateful MCP capabilities enable human-in-the-loop patterns with interrupts, sampling, and elicitation for high-stakes operations
- Production considerations include guardrails for edge agents, model quantization for memory constraints, distributed tracing for observability, and private VPC connectivity for security
These patterns enable regulated enterprises to balance data residency compliance with cloud-scale AI capabilities by strategically placing agent components across hybrid infrastructure.
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