Building AI Agents with Model Context Protocol (MCP)

AI agents are transforming how we automate tasks, but connecting them safely to real-world data and external tools remains a major challenge. The Model Context Protocol (MCP) provides an open standard to build secure, predictable, and highly capable agentic workflows. This course guides you from the fundamental concepts of agentic architecture to designing robust systems that can retrieve information, execute tasks, and respect strict security boundaries. You will gain the confidence to implement secure tool-use patterns and validation workflows in your own projects. What you'll learn: - Understand the core architecture, terminology, and security principles of the Model Context Protocol (MCP). - Configure secure connections between AI models and external data sources or APIs. - Implement structured validation workflows to ensure predictable and safe agent behavior. - Design custom tools and schemas that allow agents to reason and execute tasks effectively. - Apply modern security practices, including prompt validation and user-in-the-loop authorization. You will start by mastering foundational concepts and terminology before exploring practical, step-by-step written guides and code snippets. The curriculum progresses systematically from simple protocol connections to advanced, multi-step agent validation workflows. This course is designed for software developers, system architects, and AI enthusiasts who are new to the Model Context Protocol. No advanced background in machine learning is required. Start reading today to build smarter, safer AI agents with MCP.