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Keep consequential Codex decisions in versioned repository documents, then check each decision against the changed code and relevant test, CI, or runtime evidence. A short AGENTS.md should help Codex find the right source of truth—not carry every project rule or decision itself.
How to keep Codex decisions available across work
Information that exists only in a chat, an external document, or someone’s memory may not be available in a later Codex run. Put durable context in the repository, where it can be versioned alongside the code and discovered during work. OpenAI describes this approach in its account of harness engineering.
Use the top-level AGENTS.md as a map: state the essential repository-wide constraints and point to more specific guidance, design documents, plans, or decision records. OpenAI’s team describes an entry-point file of roughly 100 lines, but that is an example from its repository, not a universal length target. The useful test is whether a new run can follow the map to the current answer without wading through an instruction manual.
Keep documents organized by purpose and scope. Stable architectural guidance should not be mixed indiscriminately with temporary task progress. Link related documents rather than copying large passages into several places, which can drift out of sync. OpenAI’s engineering account describes separating design documents, execution plans, product specifications, generated documentation, and domain guidance, with indexes to help navigate them.
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What to put in a decision record
Use the least structure that makes a decision easy to find and verify. A small change may need only a lightweight plan; complex work benefits from a checked-in execution plan with progress and decision logs. The OpenAI Cookbook’s Codex workflow example is another official reference for iterating on development workflows. Neither source establishes a mandatory template or retention policy.
- Decision: what was agreed or approved.
- Scope: the behavior, component, or change it governs.
- Rationale and constraints: why this direction was chosen and what it must respect.
- Approval context: the owner or approval source, when relevant.
- Evidence: paths to affected files, diffs, tests, checks, or runtime observations.
- Status and follow-up: whether the decision is current, superseded, implemented, or still open, plus unresolved work.
Record an unknown as an open question rather than treating an assumption as settled. A decision record is especially useful when work spans sessions, several components depend on the choice, a later change might reverse it, or the implementation needs objective acceptance criteria.
A workflow for checking decisions against implementation
- Find the current decision. Start at repository guidance and follow its links to the applicable specification, plan, or decision record. Confirm that its scope matches the change you are about to make.
- Compare the plan with the diff. Inspect changed files and relevant lines. Trace each planned behavior to the code intended to implement it; do not infer implementation from a summary alone.
- Check the appropriate verification evidence. Inspect tests and automated checks. For behavior that depends on a running application, use reproducible runtime evidence, such as a failing reproduction followed by a confirming check. A team’s particular tools or observability setup are not guaranteed to exist in every Codex environment.
- Record the outcome. For each important decision or acceptance criterion, note whether it was checked and passed, failed, or was not checked. Link to the changed file or lines, command and result, test report, CI check, log, metric, or review comment that supports the result.
- Resolve discrepancies explicitly. If code and decision record disagree, determine whether the implementation is wrong or the decision has changed. Update the relevant source of truth and record any remaining follow-up instead of silently treating the conflict as resolved.
OpenAI’s engineering account describes local review, targeted reviews, iteration on feedback, and—in its own application—driving the UI and observing logs and metrics. These are examples of one team’s workflow, not a promise that every Codex setup has the same capabilities.
How to review a Codex pull request
The current Help Center guide, Review pull requests with Codex, recommends checking the change rather than relying on a generated summary or finding in isolation.
- Read the pull request description and summary.
- Inspect the changed files and relevant diff lines.
- Review generated findings and existing comments.
- Check test results, other checks, and unresolved merge conflicts.
- Investigate anything that needs more context, and verify generated findings against the relevant code.
- Recheck the diff and test results before commenting, committing, or merging.
Local changes can also be reviewed before a pull request is created. The Help Center says a reviewer can ask Codex to explain a change, investigate a finding, or prepare a scoped fix. Connected-repository features depend on account permissions and workspace setup; connecting GitLab or seeing a merge request does not, by itself, enable automatic GitLab cloud review.
Automate checks that can be made mechanical
Use linters or CI jobs for repeatable invariants such as documentation structure, cross-links, freshness, ownership, or architecture rules. OpenAI describes a recurring documentation-gardening process to identify stale material, as well as custom linters and structural tests for architectural constraints. Automation can flag drift; it does not replace checking whether the actual implementation satisfies the decision.
When reviews repeatedly uncover the same documentation gap, capture the lesson in the appropriate repository guide—or encode the invariant in a check if it can be tested mechanically. Keep the instruction concise and point to the deeper source rather than turning AGENTS.md into a duplicate of the entire knowledge base.
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What OpenAI’s internal figures do—and do not—show
OpenAI’s February 11, 2026 account reports that its internal beta was built with zero lines of manually written code, and estimates the work took about one tenth of the time it would have taken to develop by hand. It also reports roughly 1,500 pull requests and 3.5 pull requests per engineer per day for the described team and period. These are the team’s reported figures and estimate for its own conditions, not independently established productivity results or guarantees for other repositories. The account cautions that its end-to-end autonomous workflow depends heavily on repository structure and tooling.
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