AI Agent Architecture with the Model Context Protocol

Overview

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.

Syllabus

Foundations of Context Protocol

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.

Retrieval-Augmented Generation (RAG)

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.

Memory and Agents (The Context Pillar)

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.

Deployment and Monitoring

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.

Wrap-up: Validating Our Architectural Commitment

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.