Code Quality
AI Code Review Agent
Reviews pull requests for bugs, security issues and standards and comments inline.
Code Quality
AI QA Agent for GitHub
Your test plan runs on GitHub pull requests, and defects come back as issues or check results with evidence attached.
Test results buried in a spreadsheet or a CI log do not stop bad code from merging. This agent runs your structured test plan on GitHub pull requests or from your CI, and reports defects with evidence as pull request comments, check results or GitHub issues. Developers see what failed and why, right where they work, through a GitHub App with scoped permissions.
- Test results appear as checks on each pull request
- Defects filed as GitHub issues with evidence attached
- Scoped GitHub App, with merges left to your developers
$4,400
20–30 working days
Optional maintenance $249/mo
What it is
AI QA Agent for GitHub runs your structured test plan inside one GitHub repository, triggered from pull requests or your CI, and reports defects with evidence as pull request comments, check results or GitHub issues. It connects through a GitHub App with scoped repository permissions. Nothing ships without your approval.
Why businesses buy it
When testing happens outside GitHub, defects are found late and fixes compete with new work. Developers waste time reproducing vague bug reports. This agent runs your test plan on every pull request and posts the result as a check, with evidence attached, so problems surface before merge and each defect arrives ready to fix. Your release confidence goes up without adding a QA hire. The Scope Card fixes the repository, mapping, test workflow and trial runs before you order, so your team approves a defined, limited setup.
Problems this solves
Pull requests merge before anyone has run the test plan
The agent runs your structured test plan inside the GitHub repository, triggered from pull requests or your CI. Results arrive on the pull request before anyone clicks merge.
Defects reported in chat are hard to track later
Each defect is reported with evidence as a pull request comment, a check result or a GitHub issue. Everything stays in GitHub, linked to the change that caused it.
Teams want testing help without handing over release control
It connects through a GitHub App with scoped repository permissions, and nothing ships without your approval. You decide which pull requests and branches trigger it.
A good fit if
Your code lives in GitHub, and someone runs the same manual test plan before every merge or release.
You want test results to appear on the pull request itself, as a check or comment, rather than in a separate report.
Defects should become GitHub issues, or tickets in a tracker such as Jira or Linear, with the evidence attached.
You can create a GitHub App with scoped repository permissions and give a GitHub admin 30 minutes for setup.
Not the right fit if
You want pull requests reviewed for bugs, security issues and standards before merge. See AI Code Review Agent for GitHub.
You need failing pipelines and alerts watched and diagnosed. See AI DevOps Agent for GitHub.
Your repositories are on GitLab. See AI QA Agent for GitLab.
You have no tests yet and need unit and integration tests written. See AI Test Generation.
How it works
- A GitHub admin creates a GitHub App with scoped repository permissions, and on a 30-minute call we agree which repository data the agent reads and writes.
- Your test plan, coding standards and CI details become one automated testing workflow for a single repository and stack.
- The workflow starts where you choose, for example on each pull request or from a CI run in GitHub.
- Failures are reported with evidence on the pull request as a comment or check, or opened as a GitHub issue, depending on the mapping.
- The agent never merges or pushes to production. Any change to code waits for a person to approve it.
- Before launch, it is trialled on 20 real pull requests or pipeline runs, and you review its findings.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- A GitHub App with scoped repository permissions for the repository to test.
- A GitHub admin for a 30-minute setup call.
- Your CI/CD configuration details.
- The test plan or scenarios to run, plus your coding standards or runbooks.
- Access to your issue tracker or chat if defects should go there as well as GitHub.
- One decision-maker who approves the test plan.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified, GitHub App permissions checked, mapping agreed.
Weeks 1–2
Test workflow built for your repository and trigger.
Week 3
Pull request comments, checks and issue reporting connected.
Weeks 3–4
Trial on 20 real pull requests or pipeline runs.
Week 4
Two rounds of changes after reviewing findings.
Go-live
Live on your repository, recorded 45-minute handoff, 30 days of support.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $4,400 |
| Express | 15–25 working days | $5,150 |
| Rush | 10–20 working days | $5,950 |
What you receive at handover
- A GitHub App running your test plan on one repository, triggered by pull requests or CI
- Defect reporting as pull request comments, check results or GitHub issues
- GitHub connection plus one more system, with a documented field mapping
- Access set up through keys you control
- Test report from a trial on 20 runs, admin guide and recorded handoff
- 30 days of post-launch support
Expected benefits and how to measure them
- Can make running the test plan a normal part of every pull request.
- Helps keep defects linked to the change that introduced them.
- Pull requests tested before merge
- Compare the share of merged pull requests with a completed test run, a month before and after launch.
- Defects caught on pull requests
- Count defects raised by the agent on pull requests versus bugs found after release each month.
- Time to fix a defect
- Measure the time from a defect issue opening to its linked fix merging, before and after launch.
Before you start: Note how many pull requests are tested before merge today and how many bugs were found after release last month.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
You pay for the build once. Running it costs roughly $20 to $200 a month in your own accounts for AI usage and CI minutes, depending on how often tests run. GitHub licenses or plan upgrades are outside this build.
AI usage and CI minutes during build and testing are billed to your accounts too.
Ask about running costsCustom requirements
A second repository, a second testing workflow or custom GitHub objects beyond the agreed mapping are quoted separately. Add a note at checkout for a written Change Quote.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes and a monthly allowance of change hours, such as extending the test plan for a new feature. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A hosted testing service bills per seat or per run | A QA contractor bills for their time | Ours is one fixed price, shown before you buy |
| Where results land | A separate test dashboard | A written report after the cycle | Ours reports on the pull request, as checks, comments or GitHub issues |
| Permissions | Can ask for broad account access | Needs a contractor account | Ours uses a GitHub App scoped to the repository |
| Delivery | Quick to connect, slow to script | Weeks to onboard a contractor | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
What can AI QA Agent for GitHub read and write in GitHub?
Repositories, pull requests, issues and checks, through a two-way connection under an agreed mapping. In practice it reads the code and pull request it is testing and writes results as comments, checks or issues. Custom GitHub objects beyond the mapping are excluded unless added by Change Quote.
What permissions does AI QA Agent for GitHub need?
A GitHub App with scoped repository permissions, limited to the repository being tested and what the agreed mapping needs. A GitHub admin joins a 30-minute setup call to confirm it. GitHub licenses and plan upgrades are not included in the build price.
Will AI QA Agent for GitHub merge or change our code?
No. It runs tests and reports defects with evidence. No production change happens without a person approving it, and merging stays with your team. Findings arrive as reviewable comments, checks or issues, so a developer decides what to fix and when.
What does AI QA Agent for GitHub cost to run each month?
Roughly $20 to $200 a month for AI usage and CI minutes, billed to your own accounts and rising with how often tests run. Your GitHub plan is unchanged and there is no per-seat fee from us. The build price and delivery options are shown on this page before you order.
Can AI QA Agent for GitHub run on every pull request?
Yes, if that is the trigger you choose. It can also run from a CI pipeline run instead. The scope covers one repository and one automated testing workflow, so a second repository or a different workflow is quoted as a Change Quote before work starts.
Can AI QA Agent for GitHub send defects to Jira instead of GitHub issues?
Yes. The scope includes GitHub plus one more system, such as a tracker or chat tool like Jira, Linear or Slack. Defects can be opened there with the evidence attached, while results still appear on the pull request in GitHub for the developer who opened it.
Get test results on the pull request, not in a spreadsheet.
Order AI QA Agent for GitHub and your test plan runs against pull requests on a fixed delivery date, with defects reported as checks, comments or issues.
Works well with
Code Quality
AI Dependency Update Agent
Proposes and tests dependency upgrades as reviewable pull requests.