ChatGPT Codex Workshop: Hands-On Agentic Coding Training

By the UXreviewer.io team4 min read

A ChatGPT Codex workshop is hands-on training that shows developers how to delegate coding tasks to OpenAI's Codex agent, both locally in the terminal or IDE and as cloud tasks that run in isolated environments and return proposed changes. Participants learn to write good task briefs, configure AGENTS.md, run work in parallel and review the resulting pull requests critically.

Codex encourages a slightly different working style than purely interactive agents: you can hand off several well-defined tasks and come back to results later. That asynchronous model is efficient when tasks are scoped well and frustrating when they are not. This guide outlines what a workshop should cover and how to get value from it quickly.

What makes Codex different to train for?

As of late 2026, Codex is available through several surfaces: a command-line agent, IDE integrations and cloud tasks started from ChatGPT or connected repositories. Features and naming change frequently, so a good workshop teaches the underlying workflow rather than menu locations.

The defining trait is delegation at a distance. With cloud tasks, the agent works in its own environment while the developer continues with other work. That shifts the critical skill from steering in real time to writing a complete brief up front and reviewing a finished change afterward. For a side-by-side view of agents, read our Claude Code vs Codex comparison.

Workshop module overview

A practical Codex workshop usually combines short explanations with exercises on the team's own repository. A sensible order is:

1. Account and repository setup, including which organization accounts and data settings the team will use.
2. Environment configuration so cloud tasks can install dependencies and run tests reliably.
3. Writing an AGENTS.md file with build commands, conventions and boundaries.
4. Local agent exercises in the terminal or IDE for small, interactive changes.
5. Cloud task exercises: delegating several scoped tickets in parallel and comparing results.
6. Reviewing generated pull requests, requesting revisions and merging safely.
7. Defining team rules for which tasks go to Codex and how results are labelled.

Writing task briefs that come back right

Because cloud tasks run without constant supervision, the brief is everything. Participants practise rewriting vague tickets into briefs the agent can complete. A strong brief usually contains these elements:

• Goal: one sentence describing the user-visible or technical outcome.
• Context: the files, modules or endpoints involved and any related tickets.
• Constraints: libraries to use or avoid, patterns to follow, files not to touch.
• Definition of done: which tests must pass, which new tests to add, what the diff should not include.
• Output: a short summary of changes and open questions in the pull request description.

Environment setup is where most cloud tasks fail

A frequent source of disappointment is not the model but the environment. If the cloud container cannot install dependencies, reach a test database or run the test suite, the agent cannot verify its own work, and the results look worse than they need to.

In the workshop, teams invest time in a reproducible setup script, sensible network rules and test commands that run without manual steps. This work pays off twice: it also improves onboarding for human developers. Keep secrets out of the repository and grant cloud environments only the access they truly need.

A practical test for the workshop: pick one ticket, start it as a cloud task in a fresh environment and see whether the agent can install, build and run the tests without help. If it cannot, fix the setup before blaming the model. Document the steps in AGENTS.md so every later task benefits, and revisit them whenever dependencies or services change. Teams that treat the agent environment as a maintained piece of infrastructure, with an owner, get far more consistent results than teams that configure it once and forget it.

Running tasks in parallel without chaos

Parallel tasks are attractive, but they can produce conflicting changes and a review backlog. As a rule of thumb, start with independent tasks in different parts of the codebase, such as adding tests to separate modules or fixing unrelated bugs. Avoid sending overlapping refactors at the same time.

Another useful exercise is asking for several attempts at the same small task and comparing approaches. It teaches the team to judge solutions rather than accept the first one. Make sure reviewer capacity keeps pace: if pull requests pile up unreviewed, the team gains nothing. Our article on AI code review workflows covers how to structure that review load.

Who runs the workshop and what comes next

The UXreviewer.io team at jocapps GmbH offers agentic coding workshops for ChatGPT Codex, Claude Code, Cursor and similar tools. Trainers are AI Code Engineers who also take Codex-generated projects to production and publish apps on the Apple App Store and Google Play, so exercises reflect real delivery constraints.

After the workshop, teams typically refine AGENTS.md, tune the cloud environment and expand the list of task types they delegate. If you are planning a rollout beyond one team, see rolling out agentic coding in the enterprise. To scope a Codex workshop for your stack, book a call for free.

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