Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We teach the DevOps course mentioned below. Its page makes some performance claims that we flag and have not verified.
The useful question for a DevOps engineer is not whether AI can write Terraform. It can. The question is which parts of the job you can hand to a model, which parts need a human approval step, and how you prove that an automated fix did not make things worse. Those are the skills that separate a team using AI safely from a team that gets paged by it.
The six skills
| Skill | What you can do | What to check before trusting it |
|---|---|---|
| Infrastructure code generation | Produce Terraform, including variables, outputs and IAM, from a written spec | Run fmt, validate and plan; review IAM and encryption by hand |
| Pipelines and containers | Generate CI/CD YAML with test, scan and deploy stages, and multi-stage Dockerfiles | Non-root user, pinned base images, scan results, rollback step |
| Drift and incident agents | Compare cloud state to code, diagnose alerts and run safe runbooks | Approvals, a dry run, an audit log, a clear stop condition |
| Cost analysis | Find idle or oversized resources and estimate savings per recommendation | Never auto-apply anything with a data-loss risk |
| Security and compliance | Run CIS-style checks, look for exposed secrets, flag permissive IAM | False positives, and who owns each fix |
| Monitoring integration | Connect agents to CloudWatch-style alerts, PagerDuty and Slack | Rate limits, alert loops, and what the agent can reach |
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Set the autonomy level on purpose
A common failure with AI in operations is giving an agent more power than the team has had time to trust. A simple ladder helps.
| Level | What the agent does | Fits |
|---|---|---|
| 1. Suggest | Writes the fix; a person applies it | Anything new, or anything touching production data |
| 2. Draft a change | Opens a pull request or change ticket | Infrastructure code, pipeline edits |
| 3. Apply with approval | Applies after a named approver says yes | Known incidents with a tested runbook |
| 4. Apply automatically | Applies and reports | Low-risk, reversible actions with a rollback and a log |
Start every new agent at level 1 or 2. Move it up only after it has been right for a stretch you can point to, and keep the permission scope small.
How the AgileFever course covers it
GenAI & Agentic AI for DevOps Engineers is 12 hours and has four modules: generative AI for infrastructure automation, agentic AI for incident and cost automation, security and monitoring integration, and a project you build and deploy. It costs $450 in the US. The prerequisite is the 6-hour Python for AI course, plus working familiarity with Git, CI/CD, Docker and a cloud platform. About $10 of OpenAI credit covers the labs, there is no formal exam, and you get a certificate of completion. The page also says enrolment includes the AI Blueprint course, a 4-hour overview.
The course page states targets such as most incidents resolved automatically and 15 to 25 percent lower cloud cost. Those are the course’s goals, not promises, and the results depend entirely on your environment. Treat them as hypotheses to test on your own bills and incident history before you promise them to anyone.
Hours listed on the course pages
Related reading: DevOps Engineer to AI Infrastructure Engineer, the complete course guide and the MLOps BootCamp for DevOps engineers.
Questions people ask
Do I need machine learning knowledge?
No. The page says Python for AI is the only required prerequisite. The course assumes you already know pipelines, cloud infrastructure and the terminal.
Can an agent safely change production?
Only with limits: a narrow permission scope, an approval step for anything risky, a rollback, and logging. Begin with suggestions and pull requests.
Is this the same as the MLOps BootCamp?
No. This course is about using AI inside DevOps work. The MLOps BootCamp is about deploying and operating machine learning and LLM systems.
