A new Learn Harness Engineering course has launched, targeting developers seeking to master AI coding agent reliability. Drawing from advanced research by OpenAI and Anthropic, the program teaches how to constrain AI assistants through explicit boundaries and systematic environment design. This approach addresses reliability challenges when deploying tools like Codex and Claude Code for complex development tasks.

The course establishes closed-loop working systems rather than attempting to make models inherently smarter. Students will learn to maintain context across long-running tasks, prevent premature task completion, and implement verification through full-pipeline testing. The curriculum combines theoretical lectures with hands-on projects, including templates like AGENTS.md and feature_list.json for immediate implementation.

Participants gain skills to build features, fix bugs, and automate development with AI assistants operating under controlled conditions. The course represents a response to the growing need for more dependable AI coding tools in professional environments, offering concrete methods rather than theoretical speculation about AI capabilities.