Agentic Engineering: Delegate Real Work to AI Agents

About this course

This course contains the use of artificial intelligence."

I spend maybe 20% of my time writing code myself now. The other 80% is directing agents.

" That's roughly what a growing number of engineers started saying through 2026 — not as a hypothetical, but as a description of how their actual week changed.

The shift isn't about a smarter chatbot. It's about a different working relationship with AI: instead of asking a question and reading an answer, you hand off a piece of real work, and something with tools and a working loop of its own goes and does it — researches, builds, checks itself, and comes back with a finished result.

This course teaches that relationship, hands-on, in six lectures.

You'll learn four things, in order:

What an AI agent actually is under the hood (not marketing language — the three real parts: a reasoning core, a set of tools, and a loop that keeps it working until it's done), and a 4-stage ladder to diagnose where your own AI use currently sits on itHow to write a spec an agent can actually run with — the three-part goal packet (goal, done-condition, boundaries) that separates a vague request from one that comes back right the first time

How to review what an agent hands you, and how to build an evaluation loop so the agent catches its own mistakes before they ever reach you — including a real case where a tighter instruction, not a smarter model, cut an error rate from 41% down to 3%How to direct more than one agent at once — role separation (a builder, a critic, and a judge), and the pipeline patterns (deterministic and non-deterministic steps kept apart, checkpoints, starting small) that keep a multi-agent setup from turning into chaos

The closing lecture is about your organization, not just your own desk.

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