How OpenAI Engineers Use Codex: 7 Use Cases, 6 Workflow Practices
OpenAI published an official guide describing how its own security, frontend, API, infrastructure, and performance teams use Codex day to day. It covers seven use cases: code understanding, refactoring and migration, performance optimization, test coverage, development speed, staying focused, and exploration. The most actionable part is six best practices: start with Ask mode before switching to execution mode for large changes; maintain AGENTS.md to give Codex persistent context; write prompts like GitHub Issues, including file paths, component names, and diffs; use the Codex task queue as a lightweight to-do list; and use Best of N to compare multiple outputs. The guide quotes engineers with concrete claims, such as replacing every legacy getUserById() call and creating a PR in minutes, or overnight runs producing mergeable unit test PRs. It is vendor-authored, so it lacks failure cases and hard metrics, but the prompt templates and workflow tips are directly reusable for teams evaluating Codex.