Introduction to AWS and Production MCP Architectures
Explore deploying MCP-based multi-server architectures on AWS to build scalable, secure, and maintainable agentic AI systems. Learn to design service-to-service communication, separate logic and tools, persist context, and integrate managed AI agents like Amazon Bedrock to prepare for real-world AI deployment environments.
We'll cover the following...
Up to this point in the course, we’ve learned how the Model Context Protocol (MCP) enables agents to communicate with tools, prompts, and resources in a structured way. Most of those examples are intentionally simple and often run locally, so we can focus on understanding how MCP works without unnecessary infrastructure overhead.
This lab marks an important shift. Here, the focus moves from learning MCP concepts in isolation to seeing how they are applied in real-world, production-style environments, the kinds of systems used by teams building and deploying agentic AI in practice.
Why we are using AWS
Enterprise AI agents rarely run entirely on a developer’s laptop. Instead, they operate within ...