A project-based course and template library for engineering a reliable execution environment (harness) around AI coding agents. It provides structured lectures, hands-on guides, and reusable scripts to help developers manage state, verification, and control for agents operating across multiple sessions.
Project overview
Addresses the specific failure mode of AI coding agents breaking down on real engineering tasks due to missing environment, state management, and verification mechanisms.
Project type
AI Agent · AI Coding · Infrastructure · Learning Resources
Use cases
Coding & Development · Automation · Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Developers and AI engineers who build real implementations and need to manage environments, state, and control for AI coding agents.
Key capabilities
A reusable AI agent skill that helps scaffold a production-grade harness for a project in minutes.
A zero-dependency shell-based audit script that checks an existing repository against all five harness subsystems.
Includes a documentation site with 13 conceptual lectures and 7 hands-on project guides.
Ready-to-use copy-paste templates like AGENTS.md, init.sh, and feature_list.json.
A build pipeline to generate PDF coursebooks locally.
Provides drop-in templates (goal-template.md, loop-state-template.md, etc.) for building automated agent loops.
Limitations and risks
You will not be able to complete the projects as intended if you do not have an IDE or CLI coding agent.
Getting started
Setup difficulty is medium because it requires familiarity with terminal environments and reading or writing code. The path to first success requires running npm install, npm run docs:dev, and opening the local URL.
Evidence and sources
README: A project-based course on building the environment, state management, verification, and control mechanisms that make AI coding agents work reliably.