Hands-On Codex: Agentic Coding Workflows

AI coding agents can inspect repositories, modify code, run tests, and complete multistep tasks with minimal prompting. But effective delegation requires more than handing an agent a goal and hoping it all works out. In this course, Dr. Isil Berkun shows you how to use OpenAI Codex to build a habit-tracking application while learning a practical workflow for supervising AI agents.

Learn how to define acceptance criteria, provide repository guidance, review implementation plans, establish permission boundaries, verify outcomes, and recover from scope drift. Discover why engineering judgment remains essential, even as agents take on more execution. Interactive CoderPad challenges let you practice writing acceptance criteria and reviewing AI-generated code changes. By the end of this course, you'll have a repeatable framework for delegating coding tasks to AI agents while maintaining control over quality, risk, and scope.


Learning objectives
Define acceptance criteria for AI-assisted development tasks.
Delegate implementation work to an AI coding agent.
Apply approval and permission controls to agent workflows.
Verify AI-generated solutions against requirements.
Recognize and recover from scope drift while maintaining project goals.
Use a define, delegate, gate, verify, and recover workflow in agentic coding projects.