OpenAI Engineers Teach Codex Workflows

The real signal here is workflow, not novelty: OpenAI is codifying how to use Codex like an async teammate instead of a chat toy.

• –The “ask, then code” flow pushes users toward planning before execution, which should improve output quality on larger tasks
• –Steering mid-task is the important UX detail; it reduces the cost of imperfect prompts and makes long-running work less brittle
• –Parallel threads and task queues are the clearest productivity win for teams juggling refactors, cleanup, and prototype work at once
• –The emphasis on `AGENTS.md`, environment setup, and scoped prompts shows Codex is only as good as the context you feed it
• –This is strongest for hour-sized engineering tasks, not sprawling greenfield builds or highly ambiguous product work