Operations
AI DevOps Agent for GitHub
When a GitHub check fails, the likely cause is diagnosed and a fix arrives as a pull request or issue for approval.
A red check on GitHub usually means someone stops their work to read logs and guess. This agent watches the checks on your repository, diagnoses the likely cause when a build or deploy fails, and proposes a fix as a pull request or a GitHub issue. It works through a GitHub App with scoped permissions, follows your runbooks, and never ships anything without your team's approval.
- Failed GitHub checks diagnosed with a likely cause
- Fixes proposed as pull requests or GitHub issues
- Scoped GitHub App, and nothing ships without approval
$5,400
20–30 working days
Optional maintenance $270/mo
What it is
AI DevOps Agent for GitHub watches the checks on your GitHub repository, diagnoses the likely cause when a build or deploy fails, and proposes a fix as a pull request or a GitHub issue for your team to approve. It connects through a GitHub App with scoped permissions. Nothing ships without approval.
Why businesses buy it
Every failed check costs engineering time twice: once to investigate and again to context-switch back. On busy repositories the same failures repeat, and the person who knows the fix is not always online. This agent turns red checks into a diagnosis and a proposed fix within your GitHub workflow, so recovery is faster and knowledge from your runbooks is applied every time. The Scope Card fixes the repository, mapping, connected systems and trial on real runs before you order, so your team approves a defined setup.
Problems this solves
A red check on GitHub leaves people digging through long logs
The agent watches the checks on your GitHub repository and, when a build or deploy fails, diagnoses the likely cause. A short explanation saves engineers reading every log line.
Fixes for broken workflows get discussed but never written down
It proposes a fix as a pull request or opens a GitHub issue for your team to approve. The diagnosis and fix stay in GitHub, next to the code.
Security teams want narrow, auditable access for any new bot
It connects through a GitHub App with scoped permissions, using keys you control. Nothing ships without approval, and the field mapping documents what it reads and writes.
A good fit if
Failed checks on your GitHub repository regularly cost someone an hour of log reading before the fix even starts.
You would rather receive a proposed fix as a pull request than a bare failure notice.
You can install a GitHub App with scoped permissions on the repository or pipeline in scope.
You have runbooks, even rough ones, for how your team investigates a failed build or deploy.
Not the right fit if
You want pull requests reviewed for bugs and standards before merge. See AI Code Review Agent for GitHub.
You need production alerts grouped and incident updates drafted. See AI Incident Triage for GitHub.
Your pipelines run in GitLab. See AI DevOps Agent for GitLab.
You need several repositories or a broader infrastructure scope. Add a note at checkout for a Change Quote.
How it works
- A GitHub App with scoped permissions is installed on the agreed repository, covering pull requests, issues and checks.
- When a check fails on a build or deploy, the agent reads the output and the recent changes involved.
- It works out the likely cause using your runbooks, rather than repeating the raw error.
- If the cause is in code, it opens a pull request with a proposed fix. If not, it opens a GitHub issue describing the next step.
- The one other connected system can be your cloud alerting, so infrastructure alerts are watched too, or a chat tool for notices.
- Nothing reaches production without your team's approval. It is trialled on 20 real pipeline runs before launch.
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 on the repository in scope.
- A GitHub admin for a 30-minute setup call.
- CI/CD configuration details, including how builds and deploys are triggered.
- Runbooks or notes on how your team diagnoses failed builds and deploys.
- Access to the one other system, such as your alerting or chat tool.
- One decision-maker who approves the diagnosis and fix workflow.
- 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; repository, runbooks and second system agreed.
Weeks 1–2
Failure watching and diagnosis built around your checks.
Week 3
Fix pull requests and issue creation tested on GitHub.
Weeks 3–4
Trial on 20 real pipeline runs, results reviewed.
Week 4
Your review, two rounds of changes.
Go-live
Live; recorded 45-minute handoff; support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $5,400 |
| Express | 15–25 working days | $6,200 |
| Rush | 10–20 working days | $7,000 |
What you receive at handover
- A GitHub App watching checks on one repository and diagnosing failures
- Fixes proposed as pull requests or GitHub issues for your approval
- 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 get failing checks back to green sooner.
- Helps keep a written record of what broke and why.
- Time from red to green
- Use the repository's workflow run history to compare recovery times, a month before launch and after.
- Fix pull requests merged
- Count the agent's fix pull requests and issues, and how many your team approved.
- Recurring check failures
- Count checks failing for the same reason more than once a month, before and after launch.
Before you start: Note how often checks fail on the main branch and how long they take to fix today.
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 the agent costs roughly $20 to $200 a month in your own accounts for AI usage and CI minutes, moving with how often checks run and fail.
GitHub licenses are unchanged. AI usage during build and trial is billed to your accounts.
Ask about running costsCustom requirements
A second repository, more than one stack, wider infrastructure coverage or custom GitHub objects outside the mapping need a written Change Quote, sent of a note at checkout.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, updates, a health report and change hours, enough to update runbooks, follow new deploy steps or tune diagnoses as your pipeline changes. It starts after the 30 days of support.
$270/mo (5% of the build price a month, 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 CI insights tool bills per seat or per run | An on-call contractor bills hourly | Ours is one fixed price, shown before you buy |
| After a failure | A red check and a log | Someone investigates when available | Ours proposes a fix as a pull request or issue |
| Approval | Nothing is fixed, so nothing to approve | Depends on the access granted | Ours changes nothing without your approval |
| Delivery | Instant sign-up, slow tuning | Weeks to ramp up | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
What happens when a GitHub check fails?
The agent reads the failed check's output and the changes that led to it, compares them with your runbooks and works out the likely cause. It then opens a pull request with a proposed fix, or a GitHub issue with the next step when the cause sits outside the code.
Can the DevOps agent push fixes straight to our main branch?
No. Fixes arrive as pull requests for your team to review and merge, and infrastructure steps arrive as issues. Nothing reaches production without approval. That rule is part of the build and is checked during the trial on 20 real pipeline runs.
Can it watch cloud alerts as well as GitHub checks?
Yes, if your alerting source is the one other connected system. The agent then watches those infrastructure alerts alongside GitHub checks and handles them the same way: likely cause first, then a proposed fix or documented next step. Covering several alert sources needs a Change Quote.
What GitHub permissions does the DevOps agent need?
A GitHub App installed on the repository in scope, with the permissions agreed for pull requests, issues and checks. Your GitHub admin installs it on a 30-minute setup call. It is not set up to merge on its own, and uninstalling the app removes its access straight away.
What does the GitHub DevOps agent cost to run?
Roughly $20 to $200 a month in your own accounts for AI usage and CI minutes. A repository with frequent runs and failures sits toward the top of that range. GitHub licenses are unchanged, and the build itself is a one-time payment, with maintenance optional.
How soon can the GitHub DevOps agent start watching checks?
Delivery runs on a fixed schedule from a verified intake, and the delivery panel shows that date plus express and rush options before you buy. Installing the GitHub App and sending your runbooks within 72 hours of ordering keeps the date you chose.
Turn a red check into a proposed fix.
Order AI DevOps Agent for GitHub and failed checks on your repository come back as diagnosed fixes or issues for approval, on a fixed delivery date.
Works well with
