AI for Terraform, Kubernetes, CI/CD, and Cloud Ops
This is the hands-on core: applying AI to the surfaces production engineers actually work with. The chapters walk through using AI for Terraform, Kubernetes, and cloud operations while avoiding the common traps — generated HCL that looks clean and misconfigures state, Helm changes that drift, IAM policy edits that quietly over-permission, and cluster troubleshooting that sounds authoritative and points the wrong way.
Then we make the case that CI/CD matters more in the AI era, not less, because the pipeline is often the last place a generated change can be tested, blocked, reviewed, or rolled back before it reaches production. And we cover what to log, review, and measure — observability for AI-assisted work, including generated changes, approvals, failures, and near misses.
What you’ll learn
- How to use AI for Terraform, Kubernetes, and cloud ops without walking into the common generated-config traps.
- Why the CI/CD pipeline becomes the critical safety net for AI-generated changes.
- What to log, review, and measure for AI-assisted engineering work.
- How to catch generated changes before they reach production.
Who it’s for
DevOps engineers, SREs, and cloud engineers doing day-to-day infrastructure work who want practical, trap-aware patterns for using AI on Terraform, Kubernetes, and pipelines.
Chapters in AI for Infrastructure & CI/CD
Drawn from the working table of contents of Confidently Wrong. Subject to revision as the manuscript develops.
AI in Terraform, Kubernetes, and Cloud Ops
Practical examples of using AI for infrastructure work while avoiding common traps around generated Terraform, Helm changes, IAM policy edits, and cluster troubleshooting.
The Pipeline Is the Safety Net
How CI/CD becomes even more important in the AI era, because the pipeline is often the last place where generated changes can be tested, blocked, reviewed, or rolled back.
Observability for AI-Assisted Work
What teams should log, review, and measure when AI is contributing to engineering workflows, including generated changes, approvals, failures, and near misses.
Read the full argument.
These chapters are part of Confidently Wrong — a practical book for DevOps, SRE, platform, and infrastructure engineers on adopting AI safely without giving it unchecked authority over production.
← Back to the bookWant the same lens in podcast form? Browse the Kubernetes episodes on Ship It Weekly.