AWS for Production MCP: Building Scalable Agentic Systems

As AI agents become more autonomous, connecting them securely to data sources and tools is critical. The Model Context Protocol (MCP) provides an open standard for this integration, and AWS offers the robust infrastructure needed to run these systems at scale. In this text-based course, you will learn how to build, secure, and deploy production-grade MCP architectures on AWS. You will transition from understanding basic agentic concepts to implementing reliable, serverless environments that connect LLMs to real-world data and APIs safely. What you'll learn: 1. Understand the core concepts of Model Context Protocol (MCP) and agentic systems. 2. Configure secure AWS IAM policies and roles to enforce least-privilege access. 3. Deploy serverless MCP hosts and clients using AWS Lambda and API Gateway. 4. Manage persistent state and context storage efficiently using DynamoDB. 5. Integrate managed AI services to build responsive, context-aware applications. 6. Apply modern observability and structured logging to monitor agent behavior. The course begins with foundational definitions of MCP, agentic workflows, and basic AWS services before guiding you through step-by-step architectural patterns. You will study practical configurations and code snippets designed to help you deploy robust systems without complex setups. This course is designed for software developers, cloud enthusiasts, and AI engineers who are new to MCP and want to learn how to host agentic architectures on AWS. No prior cloud engineering experience is required, though basic programming familiarity is helpful. Start reading today to build secure and scalable infrastructure for the next generation of AI agents.