Building Agentic AI with MCP Servers

AI is shifting from passive chat interfaces to active, autonomous agents that can query databases, run APIs, and manage backend systems. To build these systems reliably, developers are turning to the Model Context Protocol (MCP) to establish secure, standardized connections between large language models and external data. This text-only course guides you from the fundamental concepts of agentic architecture to designing and deploying your own functional MCP servers. You will learn how to structure tools, manage state, and enable AI agents to execute complex, multi-step workflows with precision. What you'll learn: Understand the core architecture of Model Context Protocol (MCP) and how it standardizes AI-to-tool communication; Configure MCP servers to expose databases, APIs, and local files securely to language models; Design custom tools and schemas that allow AI agents to make autonomous decisions; Implement secure authentication and permission layers to control what actions an agent can perform; Apply agentic design patterns such as tool-use loops, routing, and error-handling strategies; Practice building backend integrations through step-by-step written tutorials and architectural walkthroughs. We begin with the foundational terminology of agentic AI and the MCP specification. From there, you will read through practical implementation strategies, exploring how to structure server responses, handle security protocols, and test your agentic workflows using clear, written code examples. This course is designed for software developers, system architects, and AI enthusiasts who want to move beyond basic prompting and build autonomous backend integrations. No prior experience with MCP is required, though a basic understanding of programming and web APIs is helpful. Start reading today to bridge the gap between language models and your backend infrastructure.