AI Agents with Model Context Protocol
Vanderbilt University · View on Coursera ↗
Taught by Dr. Jules White
Data ScienceComputer Science
About the course
Build AI Agents That Actually Work AI agents are everywhere—but most of them fail in frustrating, unpredictable ways. They get confused, waste tokens, hit dead ends, and require constant babysitting. This course teaches you the patterns and architectures that separate agents that struggle from agents that succeed. Using Python and the Model Context Protocol (MCP), you'll learn to build AI agents from the ground up—and more importantly, you'll learn why certain designs work while others fall apart. What You'll Learn: - Build MCP Tool Servers — Create the bridge that lets AI agents interact with any system: filesystems, databases, APIs, or your own custom tools - Build a Research Agent - Create your own research agent that can explore your computer and help answer questions with documents, find that missing data, or explain how code works. - Master the Agent Loop — Understand the universal pattern every AI agent follows: PERCEIVE → DECIDE → ACT → OBSERVE → REPEAT - Connect agents to tools — Wire up LLMs to discover, select, and execute tools autonomously The Patterns That Make Agents Reliable: - Response-as-Instruction — Your tools don't just return data—they guide agent behavior in real-time. Learn to design tool responses that teach the agent what to do next, when to stop, and how to communicate results. - Failing Forward — Turn errors from dead ends into stepping stones. Design error messages that teach agents how to recover—automatically, without human intervention. For the first time in computing history, your error messages have a reader that can actually do something about them. - Intelligence Budget — Every token in the context window is precious attention. Learn to maximize signal and minimize noise—pre-digesting data in tools, using scripted orchestration for mechanical work, and reserving the agent's cognitive resources for decisions that actually require intelligence.
The dissent
The most-upvoted critical review (no upvotes, 2.0★).
“I've done a few Jules White courses and I must say that this is poor and quickly thrown together course. There are a few lectures, but most of the course is just AI generated reading material. The final exam is an AI generated test too. Overall it's a poor course. I was expecting a lot more.”
Coursera's 4.5 comes from 21 ratings. Of those, 14.3% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗