Operations
AI DevOps Agent for GitLab
Failed GitLab pipelines get a likely cause and a proposed merge request, instead of a red job and a long log.
A failed GitLab job usually comes with a long log and no explanation. This agent watches pipelines in your GitLab project, works out the likely cause when a job fails, and proposes a fix as a merge request or a documented next step. It connects with a project or group access token, follows your runbooks, and leaves every merge decision with your team.
- Failed GitLab jobs explained with a likely cause
- Fixes proposed as merge requests or clear next steps
- Access token you control, with merges left to your team
$5,400
20–30 working days
Optional maintenance $270/mo
What it is
AI DevOps Agent for GitLab watches pipelines in one GitLab project, works out the likely cause when a job fails, and proposes a fix as a merge request or a documented next step for your team to approve. It connects with a project or group access token. Nothing merges without approval.
Why businesses buy it
When pipelines fail, releases stop and someone senior gets pulled in to read logs. Many failures are repeats of problems already solved once, but the fix lives in someone's head or a runbook nobody opens. This agent applies your runbooks to every failure and comes back with a likely cause and a proposed fix, so your team recovers faster and spends less time firefighting. Your GitLab plan stays the same. The Scope Card fixes the project, mapping, connected systems and trial runs before you order.
Problems this solves
Failed GitLab jobs block merges while someone reads the logs
The agent watches pipelines in your GitLab project and works out the likely cause when a job fails. Your team gets a plain explanation instead of a wall of log output.
Pipeline knowledge lives in one engineer's head
Each diagnosis comes with a proposed fix as a merge request or a documented next step. Over time, that written trail helps others handle the same failures.
Access to your GitLab project must stay narrow and in your hands
It connects with a project or group access token that you create and own. Nothing merges without approval, and the field mapping is documented for your records.
A good fit if
Your GitLab pipelines fail often enough that engineers lose time just reading job logs.
You want fixes proposed as merge requests that go through your normal review.
A maintainer can issue a project or group access token for the agreed project.
Your team has runbooks or habits for diagnosing failed jobs that can be written down.
Not the right fit if
You want merge requests reviewed for bugs and standards. See AI Code Review Agent for GitLab.
You want structured test plans run against your application. See AI QA Agent for GitLab.
Your code is hosted on Bitbucket. See AI DevOps Agent for Bitbucket.
You want logs summarized for error patterns rather than pipeline fixes proposed. See AI Log Analysis.
How it works
- A GitLab project or group access token connects the project, with pipelines and merge requests in the agreed mapping.
- Each pipeline run is watched, and a failed job triggers a diagnosis.
- The agent reads the job output and the recent commits, and uses your runbooks to name the likely cause.
- A code cause produces a merge request with the proposed fix; an infrastructure cause produces a documented next step.
- Diagnoses and next steps are posted to the one other system, such as Jira or Slack.
- Merges stay with your maintainers. The agent 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 GitLab project or group access token with the agreed scopes.
- A GitLab admin or maintainer for a 30-minute setup call.
- Your CI/CD configuration details.
- Runbooks describing how failed jobs are diagnosed and fixed today.
- Access to the tracker or chat tool where diagnoses should be posted.
- One decision-maker for approvals.
- 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; project, token scope and runbooks agreed.
Weeks 1–2
Pipeline watching and failure diagnosis built.
Week 3
Merge request proposals and posting to your second system tested.
Weeks 3–4
Trial on 20 real pipeline runs.
Week 4
Your review, two rounds of changes.
Go-live
Live, mapping documented, recorded handoff.
| 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
- The DevOps agent watching pipelines in one GitLab project
- Fixes proposed as merge requests or documented next steps
- GitLab connection through an access token, plus one more system
- Documented field mapping and configuration drawing on your runbooks
- Test report from a trial on 20 pipeline runs, admin guide and recorded handoff
- 30 days of post-launch support
Expected benefits and how to measure them
- Can reduce the time merges sit blocked by a failed job.
- Helps spread pipeline know-how beyond one person.
- Pipeline recovery time
- Compare time from a failed pipeline to the next passing one in GitLab, before launch and after.
- Accepted fixes
- Count merge requests or next steps proposed by the agent and how many your team approved.
- Failed jobs per week
- Track failed jobs on the default branch each week, starting a month before launch.
Before you start: Export a month of pipeline data from GitLab and note failure counts and recovery times.
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. After launch, expect roughly $20 to $200 a month in your own accounts for AI usage and CI minutes, depending on pipeline frequency.
GitLab licenses are unchanged. AI usage during build and trial is billed to you.
Ask about running costsCustom requirements
Additional projects, a second stack, broader infrastructure coverage or custom GitLab objects outside the mapping are priced in a written Change Quote of a note at checkout.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, updates, a health report and change hours for new pipeline stages, updated runbooks or revised diagnosis rules. It begins 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 pipeline analytics tool bills monthly | A DevOps contractor bills by the hour | Ours is one fixed price, shown before you buy |
| Failed job | A notification and a log link | Investigated when someone is free | Ours names the likely cause and proposes a merge request |
| Control | No changes are made | Depends on the access you grant | Ours waits for your maintainers to approve |
| Delivery | Quick to connect, slow to trust | Weeks to onboard | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
Does it propose fixes as GitLab merge requests?
Yes, when the likely cause is in code. The merge request carries the proposed change and the diagnosis behind it, so a maintainer can review it like any other. When the cause is infrastructure, it writes a documented next step instead and posts it to your tracker or chat.
Will it edit our GitLab CI configuration?
Only by proposing a change in a merge request, when the diagnosis points at the pipeline configuration itself. Nothing is committed to your default branch without a maintainer's approval. Settings outside the agreed mapping of projects, merge requests and pipelines are never touched.
Which GitLab token suits the DevOps agent?
This build covers one project or pipeline, so a project access token is the closest match to its scope. A group access token is fine if your team manages access at group level. Your admin picks on the 30-minute setup call and can revoke the token whenever needed.
Where do GitLab pipeline diagnoses get posted?
To the one other connected system, for example Jira or Slack, so the team sees the likely cause without opening the job log. Merge requests with proposed fixes appear in GitLab as normal. Posting diagnoses to a second destination as well needs a Change Quote.
What are the running costs of the GitLab DevOps agent?
Roughly $20 to $200 a month in your own accounts, covering AI usage and CI minutes. Projects with many pipeline runs and frequent failures sit toward the top. Your GitLab subscription is unchanged, and the build itself is paid once, with maintenance optional.
What happens before the GitLab DevOps agent works on live pipelines?
Every delivery option includes a trial on 20 real pipeline runs, so you see its diagnoses and proposed fixes before it works on live failures. The delivery panel shows the standard date plus express and rush options before you buy, and the clock starts once your intake is verified.
From failed job to proposed merge request.
Order AI DevOps Agent for GitLab and failing pipelines in your project come back with a likely cause and a proposed fix for approval, on a fixed delivery date.
Works well with
