🌍 This course is available in 15 languages: English, 简体中文, 繁體中文, 日本語, 한국어, Español, Français, Русский, Deutsch, العربية, Tiếng Việt, Oʻzbekcha, Türkçe, Portuguese (BR), Українська. Choose your language from the badges above.
🆕 What's New — July 2026
Loop Engineering Update — 1 new lecture, 1 new project
| What | Details |
|---|---|
| Lecture 13 | Why You Need to Stop Prompting Your Agent — From /goal to the six primitives of loop engineering (automations, worktrees, skills, connectors, sub-agents, external state), the generator/evaluator split, four silent costs, and a step-by-step guide to building your first loop. |
| Project 07 | Build Your First Automated Loop — Three progressive experiments: goal loop, timer loop, and maker-checker loop. Compare manual vs. automated, measure intervention reduction, and learn to step outside the loop. |
| Code templates | goal-template.md, loop-state-template.md, maker-prompt.md, checker-prompt.md — drop-in templates for building loops immediately. |
| All 15 languages | Full translation coverage across all supported languages. |
Key idea: Harness engineering builds the vehicle. Loop engineering designs the road it drives on — and you design the road from outside the car.
Learn Harness Engineering is a course dedicated to the engineering of AI coding agents. We have deeply studied and synthesized the most advanced Harness Engineering theories and practices in the industry. Our core references include:
- OpenAI: Harness engineering: leveraging Codex in an agent-first world
- Anthropic: Effective harnesses for long-running agents
- Anthropic: Harness design for long-running application development
- Awesome Harness Engineering
Quick start? The
skills/harness-creator/skill can help you scaffold a production-grade harness (AGENTS.md, feature lists, init.sh, verification workflows) for your own project in minutes.
Table of Contents
- 🆕 What's New
- ✨ Visual Preview
- What Harness Engineering Actually Means
- Quick Start: Improve Your Agent Today
- Capstone Project: A Real App
- Learning Path
- Syllabus
- Skills
- Other Courses
✨ Visual Preview
🏠 Course Homepage
A comprehensive course outline and introduction to core philosophies, providing a clear path to get started.
📖 Immersive Lectures
Deep dives into real-world pain points and hands-on projects (like Project 01) for an immersive learning experience.
🗂️ Ready-to-Use Resource Library
Templates and reference configurations designed to solve common pitfalls in multi-turn AI agent development, such as context loss and premature task completion.
PDF Coursebooks
The repository now includes a PDF build pipeline for the course content.
- Run
npm run pdf:buildto generate the currently configured PDF coursebooks locally. - Output files are written to
artifacts/pdfs/. - Run
npm run screenshots:readmeif you want to refresh the README preview images. - GitHub Actions workflow
release-course-pdfs.ymlcan build the PDFs and publish them to GitHub Releases.
The Model Is Smart, The Harness Makes It Reliable
There's a hard truth most people learn the hard way: the strongest model in the world will still fail on real engineering tasks if you don't build a proper environment around it.
You've probably seen this yourself. You give Claude or GPT a task in your repo. It starts well — reads files, writes code, looks productive. Then something goes wrong. It skips a step. It breaks a test. It says "done" but nothing actually works. You spend more time cleaning up than if you'd done it yourself.
This isn't a model problem. It's a harness problem.
The evidence is clear. Anthropic ran a controlled experiment: same model (Opus 4.5), same prompt ("build a 2D retro game editor"). Without a harness, it spent $9 in 20 minutes and produced something that didn't work. With a full harness (planner + generator + evaluator), it spent $200 in 6 hours and built a game you could actually play. The model didn't change. The harness did.
OpenAI reported the same thing with Codex: in a well-harnessed repository, the same model goes from "unreliable" to "reliable." Not a marginal improvement — a qualitative shift.
This course teaches you how to build that environment.
THE HARNESS PATTERN
====================
You --> give task --> Agent reads harness files --> Agent executes
|
harness governs every step:
|
+--> Instructions: what to do, in what order
+--> Scope: one feature at a time, no overreach
+--> State: progress log, feature list, git history
+--> Verification: tests, lint, type-check, smoke runs
+--> Lifecycle: init at start, clean state at end
|
v
Agent stops only when
verification passes
What Harness Engineering Actually Means
Harness engineering is about building a complete working environment around the model so it produces reliable results. It's not about writing better prompts. It's about designing the system the model operates inside.
A harness has five subsystems:
┌────────────────────────────────────────────────────────────────┐
│ THE HARNESS │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────────────┐ │
│ │ Instructions │ │ State │ │ Verification │ │
│ │ │ │ │ │ │ │
│ │ AGENTS.md │ │ progress.m