AI Agent Architecture with Model Context Protocol

AI models today are powerful, capable of reasoning, coding, and generating text across nearly any domain. Yet when applied in real-world settings, they often fall short. They may forget instructions, hallucinate facts, or struggle to manage large-scale enterprise data. This course addresses these challenges by introducing the Model Context Protocol (MCP), a practical framework for building AI agents that are reliable, stateful, and grounded in verifiable information. Through hands-on instructions and exercises, you will learn to design and implement the architecture behind enterprise-grade AI systems, combining memory management, Retrieval-Augmented Generation (RAG), and intelligent agent actions. You’ll also build a fully functional RAG pipeline, a session context service with a sliding window memory, and an agent executor capable of making dynamic decisions using external tools. By the end of the course, you’ll have the foundational, architectural skills to create reliable AI systems that go beyond simple chatbots, remember context, access up-to-date knowledge, and perform real-world actions reliably and efficiently

• Analyze context window constraints and token costs in LLM systems
• Design and implement the MCP framework and core services
• Build a Retrieval-Augmented Generation pipeline
• Develop an agent that uses external tools for dynamic decisions

• Basic computer and internet skills
• Ability to follow hands-on exercises in English