learn-harness-engineering

A project-based course on building the environment, state management, verification, and control mechanisms that make AI coding agents work reliably.

🌍 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 — August 2026

Frontier Harness Design Breakdowns — new section (4 breakdowns)

What	Details
New section	Frontier Harness Design Breakdowns — Apply the course's five-subsystem framework (instructions, tools, environment, state, feedback) to reverse-engineer how four frontier products build real harnesses.
Pi	How Pi builds its harness — a minimal kernel, programmable expansion, and context engineering behind "ask Pi to build what you want."
Claude Code	How Claude Code builds its harness — four-layer memory, five-level compaction, hooks, and sub-agent isolation.
Codex	How Codex builds its harness — the repository as source of truth, AGENTS.md as a directory page, and worktree isolation.
DeepSeek	How DeepSeek builds its harness — "everything is a plugin," capability seams, and an event pipeline.
All 15 languages	Full translation coverage across all supported languages.

Key idea: The course gives you a framework; these breakdowns show you how the same principles actually play out in production harnesses.

---

Graph Engineering Update — 1 new lecture, 1 new project

What	Details
Lecture 14	From Single Loops to Graph Engineering — Why a single loop grows into a graph: the four stacked layers (prompt → context → loop → graph) and where harness sits in that stack, the four parts of a graph (nodes, edges, shared state, routing), why in-loop checkpoints can't fix the three structural failures at scale (Goodhart, blindness upward, conflict), a framework-agnostic six-step walkthrough for building your first graph, graph vs. workflow, anchors, which open-source "graph engineering" projects existed before the name vs. after it, the orchestration tax, and when a graph is actually worth drawing.
Project 08	Draw Your Workflow as a Graph — Three progressive experiments: draw your maker-checker loop as an explicit graph, add a parallel fan-out/fan-in node, then add a conditional rollback edge and a human-approval node.

Key idea: A loop is a graph with one node. When your task needs specialization, parallelism, shared state, verification, and recovery — it has stopped being a loop. It's a graph.

---

🆕 What's New — July 2026

Loop Engineering Update — 1 new lecture, 1 new project

---

GitHub에서 전체 내용 보기