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.
