Can it deploy for me?

Not from the cloud: remote agents have no shell. On your own machine, a local agent with shell access could run your deploy script — but the useful, safe split is that it prepares the change and your pipeline performs it.

Which CI systems does it understand?

Any whose configuration lives in your repository — GitHub Actions, GitLab CI, Jenkinsfiles, CircleCI. Through the GitLab connector it can also read pipeline and job failures directly.

Will it find security problems in our infrastructure code?

It reads for the common ones — public buckets, open security groups, secrets in plain text, missing encryption — and names the file and line. It is a careful second read, not a compliance scan; keep a dedicated scanner in the pipeline.

Engineering role

AI DevOps Automator

An AI DevOps engineer that reads your pipelines and infrastructure code, finds the fragile parts, and drafts the fix.

It reads what your delivery actually runs on — CI workflows, Terraform, Dockerfiles, deploy scripts — and the failures they produce, then tells you why a pipeline is slow or flaky, what a deploy would change, and what to automate next. It drafts the workflow or the module; applying it stays with your pipeline and a person who owns the change.

Things you could ask for

Written the way you would actually say them. The agent plans the steps itself.

“The GitLab pipeline on main has failed three times this week. Read the failing jobs and tell me whether it is the same cause each time, and what fixes it.”
“Review our GitHub Actions workflows and list what makes a build take eleven minutes, ordered by how much time each fix would save.”
“Read the Terraform in infra/ and tell me which resources have no backups, no tags or public access, with the file and line for each.”
“Draft a GitHub Actions workflow that runs tests on every pull request, caches dependencies and blocks the merge if anything fails.”
“Every Monday, read last week’s deploys and the Grafana error panels and tell me if any release lined up with a change in errors or latency.”

What you get back

A diagnosis tied to specific jobs, files and lines, and the proposed change as a ready-to-review file or diff — a workflow, a Terraform block, a Dockerfile. From a local agent with shell access, the change can arrive on a branch with the plan output alongside it.

Tools it works with

A role is a job description; connectors are what let it do the job on your real work instead of on whatever you paste into a chat.

GitHubGive an agent your repositories — issues, pull requests and the code itself.GitLabPut an agent to work on merge requests, issues and CI failures.GrafanaGet the story out of the dashboards before the incident review.SentryTurn a wall of exceptions into an explanation and a plan.LinearLet an agent triage, plan and update Linear issues on its own.

Where it is the wrong tool

A remote agent cannot run anything. It has no shell in the cloud, so it never executes `terraform apply`, a deploy or a pipeline; it reads and drafts. A local agent with shell access in the desktop app can run commands on your machine — with *Require approval* set, you confirm each one.
It does not hold your cloud credentials. There is no AWS, GCP or Azure connector, so it sees your infrastructure as code in the repository, not the live account — drift between the two is invisible to it.
Treat every infrastructure change as a proposal. A plausible-looking Terraform diff can still replace a database; review the plan, not just the code.
It is not your CI. Pipelines should stay deterministic; the agent is for understanding and improving them, not for being a step inside one.

Starting an agent with this role

1Create a Burrak account — any plan works, and your first week is $1.
2In the Marketplace, open the Roles tab, find DevOps Automator and choose “Start an agent as DevOps Automator”. That creates a remote agent with the role’s instructions in its system prompt.
3Connect the tools it needs — GitHub, GitLab, Grafana, Sentry, Linear — from Connectors.
4Give it a brief. It keeps working in the cloud after you close the tab, and you are only charged while it is actually working.

Start for $1 How agents work

The DevOps Automator role, in practice

Can it deploy for me?: Not from the cloud: remote agents have no shell. On your own machine, a local agent with shell access could run your deploy script — but the useful, safe split is that it prepares the change and your pipeline performs it.
Which CI systems does it understand?: Any whose configuration lives in your repository — GitHub Actions, GitLab CI, Jenkinsfiles, CircleCI. Through the GitLab connector it can also read pipeline and job failures directly.
Will it find security problems in our infrastructure code?: It reads for the common ones — public buckets, open security groups, secrets in plain text, missing encryption — and names the file and line. It is a careful second read, not a compliance scan; keep a dedicated scanner in the pipeline.

Capability reference

Summarised from the role’s instructions, which are adapted from the agency-agents collection (AgentLand Contributors), used under the MIT licence.

Infrastructure as Code with Terraform, CloudFormation or CDK
CI/CD pipelines with GitHub Actions, GitLab CI or Jenkins
Zero-downtime deployment strategies: blue-green, canary, rolling
Monitoring, alerting and automated rollback as defaults
Cost optimisation through right-sizing resources

Your first 7 days for $1

Your AI assistant is waiting.
Put it to work today.

Put an agent on the work that never needed a human in the first place — the research, the reports, the follow-ups — and take the week back.

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