ChatGPT ↔ Codex Project Delegation and Validation

[urena.m.carlos]

For complex projects, allow a ChatGPT conversation to delegate an approved implementation task directly to Codex and receive the completed work back for review.

The ideal workflow would be:

Discuss → Decide → Delegate to Codex → Codex Implements → ChatGPT Reviews GitHub → Corrections if Needed → User Approves

Concrete example from my current project

I am developing the Payments Franchise Index (PFI), a web-based assessment and reporting product. Over several months, I have used ChatGPT as the product/strategy layer to develop and govern the methodology, scoring model, assessment experience, results experience, PDF reporting, language standards, and implementation requirements.

Codex is then used as the engineering layer to build the product.

Until recently, these workflows were largely disconnected. ChatGPT could help me decide what should be built, but it could not see what Codex had actually implemented. Over time, this contributed to implementation drift: older versions remained in the codebase, parts of the product were built in different locations, and some implementation no longer reflected decisions we had made in ChatGPT.

Now that ChatGPT can inspect my GitHub repositories, the workflow is substantially better. ChatGPT and I can agree on a direction, ChatGPT can prepare a precise implementation specification for Codex, and afterward ChatGPT can inspect the resulting repository and validate whether the implementation matches what we approved.

But one manual gap remains.

Today I still have to:

1. Discuss and approve the change with ChatGPT.
2. Have ChatGPT write the Codex prompt.
3. Copy the prompt.
4. Open Codex.
5. Paste and run it.
6. Wait for Codex to finish.
7. Copy Codex’s report.
8. Return to ChatGPT.
9. Paste the report.
10. Have ChatGPT inspect GitHub and validate the implementation.

The OpenAI products can already perform almost every individual part of this workflow. The missing capability is the handoff between them.

I would like to be able to approve a direction in ChatGPT and say:

“Send this to Codex.”

ChatGPT would create the Codex task using the agreed implementation specification. When Codex completes the work, the result would return to the originating ChatGPT project/conversation. ChatGPT could inspect the resulting GitHub commit/diff against the requirements established in the conversation and either:

• validate the implementation and present it to me for approval, or
• send a correction back to Codex when the implementation does not meet the agreed requirements.

The user would remain responsible for product decisions and final approval, while ChatGPT and Codex handle the implementation and validation loop.

For projects like PFI, this would turn two powerful but separate tools into a genuine end-to-end development workflow:

Human direction → ChatGPT product reasoning/governance → Codex engineering → GitHub implementation record → ChatGPT validation → Human approval

The benefit isn’t simply convenience. It would reduce context loss and implementation drift while removing the user from acting as a manual messenger between ChatGPT and Codex.

[curt.kennedy]

I wish there was a better handoff as well from Chat to Codex. Already Chat sends things over to Work, so Codex is another destination it could pick. But I also wish there was a Codex switch that would dump whatever I had in a Chat/Work over to Codex for implementation.

[nimrodfisher]

The missing handoff matters, but I think there is also a missing artifact between “we decided” and “Codex implemented.” If the approved direction only lives in the originating chat, validation later has to reconstruct which version of the requirements was actually authorized.

I’d make that boundary explicit:

1. ChatGPT produces a structured implementation plan.

2. The human reviews and approves one revision.

3. Codex receives that immutable approved revision.

4. Validation compares the implementation against the same revision, not the latest conversation state.

That would make drift visible without requiring the validation step to reconstruct intent from multiple chats. I’m curious whether the bigger pain in your workflow is preserving the approved specification or the manual transfer itself.

[curt.kennedy]

This is already the flow, for the most part, have you been using Codex?

The part that is missing is the handoff to Codex. Why? Well Codex is linked to your repo and can work PR’s, that’s what it does.

[nimrodfisher]

Fair correction… I have used Codex of course, but I overstated what is missing.

Codex already owns the repository handoff and implementation flow. The narrower question I was trying to get at is whether the approved intent survives that handoff as a durable reference: which plan revision was reviewed, what changed during review, and whether the resulting PR can be checked against that exact revision rather than reconstructed from the conversation.

That may already be sufficiently covered by the existing plan and PR workflow for most users. I’d be interested in how you preserve that trace when the plan changes after review

[curt.kennedy]

I would say what I do now, in the scenario of accidentally starting a Chat that needs to turn into a Codex session … is stop chatting in Chat … and either copy relevant content over to Codex and have Codex enter Plan mode with /plan, this is the “immutable contract” you are wanting.

Then Codex will ask you a bunch of questions and writes out the plan. So it would be nice if I could avoid the copy-paste, and just fork the whole conversation over to Codex, plan it, compact it if I need to before coding.

[nimrodfisher]

That makes sense.

In your workflow, `/plan` is already the contract, and the missing piece is preserving the full conversation context when moving into Codex without manual copy-paste.

One thing I’m curious about… once Codex turns that context into a plan, do you usually accept the first version, or is there still a review loop where you challenge assumptions and revise the plan before coding?

I’m trying to understand whether the handoff is the whole problem, or whether plan review becomes a separate bottleneck afterward.

[curt.kennedy]

I don’t always accept the first draft of the plan. Yes, you can interact with the AI indefinitely to update the plan to your satisfaction before the AI executes it.

“The Plan” is literally a file as well, and you can commit/push it into your repo. You can create another instance of an AI model and feed the same plan to it as well. Think of the Plan as big overarching System Prompt. Depending on the project, or needs, this document can be a page long, all the way to a small book in length.

For repetitive tasks, I have the AI create a “run sheet” with details, and then have the AI run it, and then ask the AI to edit the run sheet for the “next AI” to run. The AI will pass on any knowledge missing in the plan that wasn’t there before. And over time, the plan evolves and becomes more useful.

[nimrodfisher]

That is much closer to the workflow I was trying to understand. The evolving run sheet is especially interesting: it is not just a one-time plan, but a durable artifact that carries learning into the next execution.

When that file grows from a page into something much larger, how do you review changes between versions?

Do you rely on Git diffs and chat history, or do you have a way to attach a decision or comment to one specific section and preserve why it changed?

I’m building around that review boundary, so I’m trying to understand whether version-to-version comprehension is a real problem in workflows like yours or whether the file + Git already handles it well enough.

[curt.kennedy]

Well, that’s what makes AI different. You can of course use whatever diff tool you like, but if you don’t know, just ask the AI, assuming it is in some sort of IDE or in the ChatGPT desktop app, for example, here in the ChatGPT app: