Operations
AI DevOps Agent for Bitbucket
Watches pipelines and alerts, diagnoses failures and proposes fixes, connected natively to Bitbucket so it reads and updates repositories, pull requests and…
Operations
AI DevOps Agent
Watches your pipelines and alerts, diagnoses failures as they happen, and proposes a fix for a human to approve.
A failed build or deploy at the wrong moment can stall a whole team while someone digs through logs. AI DevOps Agent watches your CI/CD pipelines and infrastructure alerts, works out the likely cause when something fails, and proposes a fix as a pull request or a documented next step. Your team approves every change, and nothing touches production without a person saying yes.
- Failed builds and deploys diagnosed as they happen
- Proposed fixes arrive as pull requests or clear next steps
- Every change approved by a person before production
$4,900
20–30 working days
Optional maintenance $249/mo
What it is
AI DevOps Agent watches your CI/CD pipelines and infrastructure alerts, diagnoses the likely cause when a build or deploy fails, and proposes a fix as a pull request or a documented next step for your team to approve. Nothing changes in production without approval.
Why businesses buy it
Pipeline failures are expensive interruptions. An engineer drops their work, reads through long logs and often fixes the same class of problem as last month. Meanwhile releases wait. AI DevOps Agent does the first diagnosis for you and arrives with a proposed fix, so recovery is faster, repeat issues are handled consistently, and engineers spend their time building. The Scope Card fixes the repository, connected systems, workflow and trial on real runs before you order, so you know exactly what it watches and what it can propose.
Problems this solves
Failed builds and deploys take ages to diagnose
The agent watches your CI/CD pipeline and infrastructure alerts. When a build or deploy fails, it works out the likely cause and explains it, so engineers start from a lead.
The same pipeline failures keep coming back
Along with the diagnosis, it proposes a fix as a pull request or a documented next step. Repeat problems get a written fix that your team can approve once.
Automated fixes to infrastructure feel too risky to allow
Nothing changes in production without approval. Every proposed fix waits for a person, and guardrails set during the build keep the agent within agreed limits.
A good fit if
Your pipelines fail regularly enough that someone spends real time each week just working out why, before they can even start fixing it.
You want a diagnosis of the likely cause, not just an alert saying something broke, when a build or deploy fails.
You have infrastructure across AWS, GCP or Azure, or a CI/CD setup, that alerts should be watched against.
You are comfortable with the agent proposing a fix as a pull request or documented step, with no production changes made without your team's approval.
Not the right fit if
You want pull requests reviewed for bugs and standards generally, not pipeline and infrastructure failures specifically. See AI Code Review Agent.
You want dependency versions checked and proposed as routine upgrades, not broader pipeline diagnosis. See AI Dependency Update Agent.
You need test plans run against your application, not pipeline failures diagnosed. See AI QA Agent.
You want new tests written for existing code, not infrastructure operations covered. See AI Test Generation.
How it works
- You give us scoped access to one repository or pipeline, your CI/CD configuration, and your infrastructure alerts across systems such as AWS, GCP or Azure.
- The agent watches builds, deploys and infrastructure alerts continuously, so a failure is seen the moment it happens, not after someone notices manually.
- When something fails, it reads the logs and recent changes to work out the likely cause, rather than surfacing a bare error message.
- A fix is proposed, either as a pull request when the cause is in the code, or as a documented next step when it is infrastructure.
- Findings and proposed fixes post to your issue tracker or chat, such as Jira, Linear or Slack, so nothing sits unseen in a log.
- No production change happens without your team's approval. We trial the agent on 20 real pull requests or 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.
- Scoped repository access, as an app or token, plus access to your CI/CD configuration.
- Your infrastructure alerting setup, across systems such as AWS, GCP or Azure, that the agent should watch.
- Coding standards or runbooks describing how your team usually diagnoses and fixes a failed build or deploy.
- Access to your issue tracker or chat tool, such as Jira, Linear or Slack, where findings should be reported.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can approve the diagnosis and fix workflow before launch.
- 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 confirmed, with repository, infrastructure alerts and runbooks agreed.
Weeks 1–2
Pipeline and alert watching built and connected across your repository and infrastructure.
Week 3
Diagnosis, fix-proposal and tracker or chat reporting connected and tested.
Weeks 3–4
Trialled on 20 real pull requests or pipeline runs, with findings reviewed together.
Week 4
Your review and two rounds of changes.
Go-live
Live, with a recorded handoff session, and 30 days of support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $4,900 |
| Express | 15–25 working days | $5,750 |
| Rush | 10–20 working days | $6,600 |
What you receive at handover
- The DevOps agent watching one repository or pipeline and its alerts
- Connection to your Git host and one tracker or chat tool
- Prompt and guardrail configuration drawing on your runbooks
- Test report from a trial on 20 pipeline runs
- Admin guide and recorded 45-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can shorten the time a broken pipeline blocks your team.
- Helps turn recurring failures into fixes that stick.
- Time to restore a failed pipeline
- Compare time from failure to next green run in your CI history, a month before and after launch.
- Repeat failures
- Count failures with the same cause appearing more than once in a month, before launch and after.
- Proposed fixes accepted
- Track how many of the agent's pull requests or next steps your team approved each month.
Before you start: Export a month of pipeline history and note failures, causes and time to recover.
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, covering AI usage and CI minutes, and it moves with how often pipelines run and how much infrastructure it watches.
AI usage during the build and testing is billed to your own accounts too, so your AI model and CI relationships stay in your name.
Ask about running costsCustom requirements
If you need a second repository, more than 2 connected systems, or a broader infrastructure scope, add a note at checkout for a written Change Quote. Your order still runs on its normal timeline. Larger or bespoke projects go through the Custom AI request form.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, updates, a health report and change hours for things such as a new alert source or updated runbooks. 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 pipeline-monitoring SaaS tool charges per seat every month, failing or not | An on-call DevOps contractor bills by the hour | Ours is one fixed price, shown before you buy |
| Diagnosis | A monitoring tool alerts you that something failed and stops there | A contractor's diagnosis depends on who is on call | Ours reads logs and recent changes and proposes the likely cause |
| Fix proposal | A monitoring tool leaves the fix entirely to your team | A contractor's fix depends on availability and hours billed | Ours proposes a pull request or a documented next step |
| Delivery | A monitoring tool is instant to sign up but slow to tune well | A DevOps engagement usually takes several weeks to ramp up | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
How is AI DevOps Agent different from AI Dependency Update Agent?
AI Dependency Update Agent is narrow and background-only, proposing tested dependency upgrades as pull requests. AI DevOps Agent watches your whole pipeline and infrastructure alerting, diagnoses the likely cause of any failure, and proposes a fix, not just a dependency bump. Start here if pipeline and infrastructure failures generally, not just dependencies, are the problem.
What does AI DevOps Agent mean by 'diagnosing the likely cause'?
When a build or deploy fails, the agent reads the logs and recent changes to work out what most likely broke, rather than surfacing a bare error message for someone to interpret. That diagnosis is attached to the fix it proposes, so your team starts from a working theory instead of a blank investigation.
Does AI DevOps Agent fix production issues automatically?
No. It proposes a fix, as a pull request when the cause is in the code or as a documented next step when it is infrastructure, and your team approves it. No production change happens without that approval, which is a fixed part of the build, not an optional setting.
How much does AI DevOps Agent cost to run each month?
Roughly $20 to $200 a month in your own accounts, covering AI usage and CI minutes, and it moves with how often pipelines run and how much infrastructure it watches. The build itself is paid once and covers connecting your repository, CI/CD and alerting.
Which infrastructure providers does AI DevOps Agent support?
AWS, GCP and Azure are the common ones we connect to, alongside your Git host and CI/CD configuration. You name the systems involved during intake, up to 2 connected systems beyond the repository itself, and we scope access to exactly those.
How long until AI DevOps Agent is watching our pipelines?
Delivery runs on a fixed schedule from a complete intake, and the delivery panel shows that date plus express and rush options before you buy. Most of the time goes into connecting your repository and infrastructure alerts, building the diagnosis workflow, and trialing it on 20 real runs before launch.
A diagnosis and a proposed fix, not just an alert.
Order AI DevOps Agent and it goes live on a fixed delivery date, watching your pipelines and proposing fixes for your approval.
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