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.
AI Agent Architecture with the Model Context Protocol
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Analyze the LLM Context Window constraint and token cost as primary drivers for specialized architecture.
Design and implement the MCP Server/Client framework and construct two core services (RAG and Sliding Window Cache) for efficient context management.
Develop an intelligent agent that uses a tool protocol for dynamic, context-aware decision-making.
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Recently updated!
March 2026
Taught in English
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There are 5 modules in this course
Welcome to Module 1: Foundations of Context Protocol. This module is the most critical in the course because we're not just defining a technology; we're establishing the architectural imperative for why the Modular Context Protocol (MCP) must exist. If you understand the fundamental constraints we cover here, the rest of the course—RAG, Memory, Agents—will click into place instantly.
What's included
2 videos2 assignments
Welcome to Module 2! We're now focusing on the first major capability of the Modular Context Protocol: Retrieve, powered by Retrieval-Augmented Generation (RAG). We will learn how RAG overcomes core LLM limitations by retrieving only the most relevant, verifiable information at query time.
What's included
1 video1 reading2 assignments
This module focuses on the Context pillar of the Modular Context Protocol (MCP). We are moving beyond retrieval to give the LLM the ability to remember (Memory) and act (Agents), making it a truly stateful and useful assistant.
What's included
1 video1 reading2 assignments
This module transitions the MCP architecture from a functional prototype to an enterprise-grade production system. It focuses on the "Three Horsemen of Production" to ensure AI agents are fast, trustworthy, and measurable. We will learn to implement scientific evaluation frameworks and robust defense mechanisms against malicious inputs.
What's included
2 videos2 readings3 assignments
In this final module, we will synthesize the Modular Context Protocol (MCP) framework, integrating RAG, Sliding Window Memory, and Agent Routing, into a single, high-performance system. This is the transition from building individual components to mastering a cost-controlled, enterprise-grade AI architecture.
What's included
1 video1 assignment
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