AI Agent Architecture with the Model Context Protocol

Instructor: Paddu Melanahalli

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5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

5 modules

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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Assessments

10 assignments¹

AI Graded see disclaimer

Taught in English

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There are 5 modules in this course

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.

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

2 videos Total 16 minutes

🎥 Deployment, Monitoring, and Enterprise-Grade AI Production 8 minutes
🎥 Modular Context Protocol (MCP) Agent Routing Exercise 8 minutes

2 readings Total 20 minutes

💻 Modular Context Protocol (MCP) Agent Routing Exercise 10 minutes
🔐 Feedback: The Production Checklist 10 minutes

3 assignments Total 90 minutes

🤔 Reflection: Modular Context Protocol (MCP) Agent Routing Exercise 30 minutes
❓ Module 4 Quiz 30 minutes
✍️ Activity: The Production Checklist 30 minutes

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.