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Where should Codex decisions live?
Put decisions that need to survive a chat or work session in the repository: Markdown documentation, plans, specifications, schemas, or other versioned artifacts. OpenAI’s account of its engineering workflow describes repository files as available to Codex during a run, while information kept only in chat, external documents, or someone’s memory may not be in context (OpenAI, “Harness engineering,” February 11, 2026).
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Treat the repository as the working source of truth. A decision record should be findable from the files Codex is likely to encounter, and it should point to the relevant code, tests, or related documentation rather than duplicate large passages.
Use AGENTS.md as a map
Keep the top-level AGENTS.md focused on entry-point guidance: where to find the governing design or plan, which constraints matter, and how to verify work. Link to detailed sources rather than trying to fit every specification and historical decision into one instruction file.
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OpenAI reports that its own short AGENTS.md is roughly 100 lines. That is an example from one team, not a universal limit or target. The useful measure is whether someone can quickly find the right source of truth.
Choose the record’s level of structure
For a small change, a lightweight task plan may be enough. For work spanning components, sessions, or multiple decisions, keep a versioned execution plan with progress and decision notes. OpenAI describes separating design documents, product specifications, execution plans, generated documentation, and domain guidance, with indexes to connect them (OpenAI Cookbook, “Iterating development workflows with Codex”).
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Record a decision when a future change might otherwise reverse it, when other components depend on it, or when the task needs explicit acceptance criteria. Keep stable architectural choices distinct from temporary task status. There is no single required template; use only enough structure to make the choice and its verification clear.
What should a decision record contain?
For consequential changes, capture the choice, the part of the system it governs, and how someone can tell whether the implementation follows it. A concise record can use these fields:
- Decision: the approved behavior or direction.
- Scope: the component, workflow, or change covered.
- Rationale and constraints: why the choice was made and what it must preserve.
- Evidence: relevant file paths or diff lines, test and check results, runtime observations, or review discussions.
- Status and follow-up: whether it is current, implemented, superseded, or still open, plus any unresolved next action.
This is a practical format, not an OpenAI-mandated schema. If a needed decision is absent, record it as an open question instead of treating an assumption as approved.
How do you check that the code matches the plan?
Trace the intended behavior into the changed files, then check the evidence appropriate to the claim. For a code-level invariant, that may mean inspecting the implementation and targeted tests; for user-visible behavior, it may also require a reproducible runtime check. A passing test does not establish behavior it never exercises, and a written plan does not establish that any check passed.
- Find the governing decision. Start at
AGENTS.mdand follow its links to the applicable specification, plan, or decision record. Confirm that it is current and applies to the files being changed. - Inspect the diff and implementation. Review the changed files and relevant lines. Follow the path from the planned behavior to the code that implements it; note behavior that is missing, broader than intended, or inconsistent with the decision.
- Check tests and automated results. Inspect relevant test results, CI checks, and any other automated evidence. Match each acceptance criterion to a check that actually covers it.
- Verify runtime behavior when needed. If the claim concerns observable behavior, reproduce the issue or scenario and record the confirming result. Logs or metrics can help where the system exposes them; available tooling varies by repository and environment.
- Record the outcome. For each important criterion, note whether it was checked and passed, failed, or was not checked. Link the result to its supporting path, command output, report, runtime observation, or review comment.
OpenAI describes local review, targeted follow-up reviews, and iteration on feedback in its engineering account; its particular tools and end-to-end workflow are examples of one team’s environment, not capabilities every Codex setup will have (OpenAI, “Harness engineering”).
How should you review a Codex pull request?
OpenAI Help Center’s guidance, updated shortly before October 7, 2026, recommends reviewing the PR description and summary, changed files and relevant diff lines, findings and existing comments, test results and checks, and unresolved merge conflicts. Investigate anything that needs more context, and compare generated findings with the relevant code before relying on them (“Review pull requests with Codex”).
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After addressing feedback, inspect the resulting diff and test results again before commenting, committing, or merging. You can also ask Codex to explain a change, investigate a finding, or prepare a scoped fix, then review the resulting evidence yourself.
Connected-repository features depend on account permissions and workspace setup. The Help Center notes that connecting GitLab or seeing a merge request does not enable automatic GitLab cloud review. Check the current product guidance for availability in your environment.
How can you keep repository knowledge reliable?
Documentation can become stale even when it was accurate when written. Where practical, add linters or CI checks for document structure, cross-links, freshness, or ownership. Encode architecture rules in custom linters or structural tests when they can be checked mechanically. OpenAI describes using these approaches and recurring documentation maintenance to catch stale material and address gaps that repeatedly appear in feedback (OpenAI, “Harness engineering”).
- Link to one authoritative decision instead of maintaining conflicting copies.
- Distinguish current guidance from completed plans and historical decisions.
- Automate repeatable checks, but still inspect the actual code and relevant results.
- When a decision changes, update its status and the documentation that routes readers to it.
OpenAI’s engineering account also reports that its internal beta was built with zero lines of manually written code, that the team estimated the work took about one tenth of the time it expected for hand-written development, and that it handled roughly 1,500 pull requests at 3.5 per engineer per day. These are figures for that team and its conditions, not independent benchmarks or predictions for another repository. The account cautions that its autonomous workflow depends on its specific repository structure and tooling.
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