---
title: "Review the diff since the last release tag before shipping"
description: "Gate each release on an agent review of the diff between the previous tag and the default branch."
url: https://starsling.dev/ci-playbooks/techniques/release-readiness-review
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/release-readiness-review
---

# Review the diff since the last release tag before shipping

Gate each release on an agent review of the diff between the previous tag and the default branch.

## How to choose an approach

This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it.

- **[OpenAI](https://starsling.dev/ci-playbooks/openai#release-readiness-review)**: Use it when Releases ship from the default branch after a manual skim of the changelog. Check before adopting: Treat a green call as one reviewer's finding and keep the release checks your CI already runs.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Review the diff since the last release tag before shipping.

Gate each release on an agent review of the diff between the previous tag and the default branch.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/release-readiness-review.md

Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence.
Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured.
Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo.
Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES.
Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch.
Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge.
````

## How OpenAI used this technique

Read what OpenAI changed and follow the original sources.

### OpenAI: Review the diff since the last release tag before shipping

The release review in the Agents SDK repositories finds the previous release tag, diffs it against the latest main, and has the agent inspect that diff for backward compatibility issues, regressions and the migration or release-note updates a change needs before making an overall release-readiness call. A blocked call comes with a specific unblock checklist. In openai-agents-python#2480 the report sits in the pull request description: a green call, with the Python 3.9 drop rated moderate and the release-note follow-up it requires.

Evidence: Source PR/commit evidence

The release pull request carries a release readiness report with a green call, the Python 3.9 drop rated moderate and the release-note follow-up it requires ([Source](https://github.com/openai/openai-agents-python/pull/2480))

Use it when: Releases ship from the default branch after a manual skim of the changelog.

Before applying it: Treat a green call as one reviewer's finding and keep the release checks your CI already runs.

[Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#release-readiness-review)

[Primary source](https://github.com/openai/openai-agents-python/pull/2480)

## CI optimization is continuous work.

Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on.

[Get self-improving CI](https://starsling.dev/products/runners)

## Company Playbooks

### [Anthropic](https://starsling.dev/ci-playbooks/anthropic)

Run only affected tests

### [DoorDash](https://starsling.dev/ci-playbooks/doordash)

Run build and test in parallel, sharded across CI agents

### [Linear](https://starsling.dev/ci-playbooks/linear)

Benchmark the same job on faster hardware

### [Mastra](https://starsling.dev/ci-playbooks/mastra)

StarSling runners for the slowest test workflow

### [OpenAI](https://starsling.dev/ci-playbooks/openai)

Rank CI fixes by the minutes they save

### [Ramp](https://starsling.dev/ci-playbooks/ramp)

Move a CPU-bound test suite to a faster CPU

### [Shopify](https://starsling.dev/ci-playbooks/shopify)

Let an agent iterate against one CI metric

### [Spotify](https://starsling.dev/ci-playbooks/spotify)

Build arm64 on native arm64 runners

### [Stripe](https://starsling.dev/ci-playbooks/stripe)

Run only the tests a change can reach

### [Uber](https://starsling.dev/ci-playbooks/uber)

Build the merge-queue speculations most likely to be needed