Agentic Engineering: From MCP Servers to Agent Factories

About this course

This course contains the use of artificial intelligence.

Most AI courses teach you to talk to a model.

This one teaches you to build the engineering around it: the part that decides whether your system still works at 3 a.m. on someone else's machine.11 sections · 10 build assignments · 7 auto-graded coding exercises · a capstone you ship

Here's the build order:

The three actors. Engineers plan and review, agents execute with judgment, and deterministic code verifies. This is the placement diagnostic you'll run on every task from now on.

Design before code, with BMAD. Write a spec a machine can check, because an agent doesn't slow down at an ambiguity. It implements it.

The loop. Write a working agent in about fifty lines, then break it on purpose four ways. Each failure is a production lesson, and no better model fixes any of them.

MCP. Design your tools on paper, build the server and publish it. Then run the cold-install test: a stranger installs it from your README while you stay silent.

No API? Automate the file format. The save format is the API. You prove it with a byte-identical round trip.

Agents as typed programs, and token economics. Inputs are validated before you spend a token, outputs are enforced by a schema, and agents are composed as each other's tools. You also work through why a ten-turn run costs so much and learn the three levers that fix it.

Production agents. The model produces data and your code produces the side effects. You'll cover idempotency and trust boundaries, and drill every failure path.

Data and AI.

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