Developing Reliable AI Agents with Model Context Protocol (MCP)

As AI agents become core components of modern software, ensuring their behavior remains predictable and reliable is a major challenge. This written course teaches you how to design, build, and maintain deterministic agents using the Model Context Protocol (MCP) to connect language models with external tools and databases. You will learn to: Understand the foundational concepts of the Model Context Protocol (MCP) and agentic architectures; Configure structured outputs and JSON schemas to ensure predictable model responses; Connect language models to external data sources and APIs using secure protocol standards; Implement quality metrics to evaluate and test agent performance systematically; Apply robust error-handling strategies to manage unexpected model behaviors. This course begins with essential terminology and architectural fundamentals before guiding you through the practical steps of tool integration, output validation, and systematic testing. Designed for software developers and AI enthusiasts new to agentic workflows, this course requires no prior experience with MCP. Start reading today to build robust, production-ready AI agents.