Operations
AI DevOps Agent for Bitbucket
Bitbucket Pipelines failures come with a likely cause and a proposed pull request, ready for your team to approve.
When a Bitbucket Pipelines step fails, someone has to stop, read the log and work out what broke. This agent watches Pipelines on your repository, diagnoses the likely cause of each failed build or deploy step, and proposes a fix as a pull request or a written next step. It can post to Jira or Slack, and every change waits for your team's approval.
- Failed Pipelines steps explained with a likely cause
- Proposed fixes as pull requests or written next steps
- Updates posted to Jira or Slack, with changes approved by you
$5,400
20–30 working days
Optional maintenance $270/mo
What it is
AI DevOps Agent for Bitbucket watches Bitbucket Pipelines on one repository, diagnoses the likely cause when a build or deploy step fails, and proposes a fix as a pull request or a written next step for approval. It connects with a Bitbucket OAuth consumer or app password. Findings can also go to Jira or Slack.
Why businesses buy it
Failed Pipelines runs block releases and drain focus, especially when the same few problems keep coming back. Without a clear diagnosis, engineers lose valuable time just working out what happened. This agent gives you that diagnosis and a proposed fix straight away, following your own runbooks, so recovery is faster and fixes are consistent. Your Bitbucket plan stays as it is. The Scope Card fixes the repository, mapping, connected systems and trial runs before you order, so your team knows exactly what it will do.
Problems this solves
Bitbucket Pipelines fail and nobody knows where to start
The agent watches Bitbucket Pipelines on your repository and diagnoses the likely cause when a build or deploy step fails. Engineers start from a clear explanation.
Pipeline problems never reach the people planning the work
Findings can also go to Jira or Slack. A failed step can then be planned or discussed where your team already works, alongside the proposed fix.
You want suggested fixes without anything changing on its own
Each fix is proposed as a pull request or a written next step, and waits for approval. It connects through a Bitbucket OAuth consumer or app password you control.
A good fit if
You build and deploy with Bitbucket Pipelines, and failed steps stall releases while someone digs through logs.
Your team plans work in Jira and wants diagnoses to land there.
An admin can create an OAuth consumer or app password scoped to the repository.
You keep runbooks for common build and deploy failures, even informal ones.
Not the right fit if
You want pull requests reviewed against coding standards. See AI Code Review Agent for Bitbucket.
Your repositories are on GitHub. See AI DevOps Agent for GitHub.
You want on-call engineers helped with runbooks and context during incidents. See AI On-Call Assistant.
You need more than one repository or stack covered. Add a note at checkout for a Change Quote.
How it works
- A Bitbucket OAuth consumer or app password connects the repository, with pipelines and pull requests in the agreed mapping.
- Every Pipelines run is watched, and a failed build or deploy step starts a diagnosis.
- The step's output and the commits involved are read against your runbooks to find the likely cause.
- A code fix is proposed as a Bitbucket pull request; an environment or infrastructure cause becomes a written next step.
- Diagnoses are posted to the one other system, for example Jira, so they join your normal planning.
- No change ships without approval. A trial on 20 real pipeline runs comes 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 Bitbucket OAuth consumer or app password scoped to the repository.
- A Bitbucket admin for a 30-minute setup call.
- Your CI/CD configuration details for Pipelines.
- Runbooks for diagnosing failed builds and deploys.
- Access to Jira, Slack or the tool where diagnoses should go.
- 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; repository, runbooks and destination for diagnoses agreed.
Weeks 1–2
Pipelines watching and diagnosis steps built.
Week 3
Pull request proposals and Jira or Slack posting tested.
Weeks 3–4
Trial on 20 real pipeline runs.
Week 4
Your review and two rounds of changes.
Go-live
Live, 45-minute handoff recorded, support begins.
| 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 Bitbucket Pipelines on one repository
- Fixes proposed as pull requests or written next steps for approval
- Bitbucket connection through an OAuth consumer or app password, plus Jira or Slack
- Documented field mapping and test report from a trial on 20 runs
- Admin guide and recorded handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can shorten the time a failed step holds up a release.
- Helps failures get picked up in Jira or Slack instead of being missed.
- Pipeline recovery time
- Compare time from a failed Bitbucket Pipelines run to the next successful run, before launch and after.
- Failures with a logged follow-up
- Count failed runs that have a Jira issue or Slack thread attached each month.
- Fixes approved
- Count pull requests or next steps proposed by the agent and how many your team approved.
Before you start: Note last month's failed pipeline runs, their causes and how long each took to resolve.
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, rising with build frequency.
Bitbucket licenses and plan upgrades are outside the build. AI usage during build and trial is billed to your accounts.
Ask about running costsCustom requirements
More repositories, another stack, wider infrastructure coverage or custom Bitbucket objects outside the mapping need a written Change Quote. Add a note at checkout and it comes back.
Request a custom versionKeep it running
Monthly maintenance for this edition covers monitoring, fixes, updates, a health report and change hours, such as new deployment steps or updated runbooks. 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 build monitoring add-on bills monthly | An outsourced DevOps team bills a retainer | Ours is one fixed price, shown before you buy |
| Failed step | A failed status and a log | A ticket to investigate | Ours proposes a pull request or next step |
| Planning | Alerts outside your tracker | Status emails | Ours posts diagnoses to Jira or Slack |
| Delivery | Connect fast, tune slowly | Weeks to agree scope | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
What does it do when a Bitbucket Pipelines step fails?
It reads the failed step's output and the commits in that run, checks them against your runbooks and names the likely cause. A cause in code becomes a proposed pull request; an environment or infrastructure cause becomes a written next step, posted to your tracker or chat.
Can diagnoses from Bitbucket Pipelines land in Jira?
Yes, if Jira is the one other connected system. Each diagnosis and next step is posted there so it joins your normal planning, while proposed code fixes stay as pull requests in Bitbucket. Slack is the alternative if your team prefers a channel to a ticket.
Will the Bitbucket DevOps agent deploy fixes itself?
No. It proposes; your team approves. Proposed code changes arrive as pull requests that follow your usual review, and infrastructure steps are written up for a person to carry out. No production change happens without approval, and that is tested in the 20-run trial.
Which Bitbucket credential does the DevOps agent use?
A Bitbucket OAuth consumer or app password, as listed on the Scope Card, created by your admin and scoped to the agreed repository. We confirm the method your workspace supports on the 30-minute setup call, and your admin can revoke access whenever needed.
What does the Bitbucket DevOps agent cost to run?
Roughly $20 to $200 a month in your own accounts for AI usage and CI minutes. Repositories that run Pipelines many times a day sit toward the top. Bitbucket plans are unchanged, and the build itself is a single payment, with maintenance optional.
How soon can the Bitbucket DevOps agent go live?
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. The credential, CI/CD details and runbooks are the intake items that let the build start, so send them within 72 hours of ordering.
Every failed Pipelines run, explained and ready to fix.
Order AI DevOps Agent for Bitbucket and failed Pipelines runs come back with a likely cause and a proposed fix for approval, on a fixed delivery date.
Works well with
