Udemy – Agentic Engineering: Delegate Real Work to AI Agents

“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 it
• How 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