For DevOps and platform engineering teams, what AI-powered tools or workflows are actually useful in day-to-day work? I'm especially interested in practical examples involving CI/CD, infrastructure as code, pull-request reviews, testing, monitoring, or other operational tasks that are already being used in real time.
2 Answers
AI is most useful as an assistant around deterministic automation rather than as a replacement for the pipeline itself. It can help create or improve CI/CD workflows, explain failures, suggest infrastructure-as-code changes, and provide feedback on pull requests. The final pipeline steps should remain predictable, testable, and controlled by normal rules.
We’ve had good results adding multiple AI review agents to the CI process. They look for different categories of problems, such as bugs, risky changes, missing tests, and security concerns, which gives the team another layer of review before merging.
How are you handling the review results? Can an agent block a change when it identifies a serious issue, or does it only publish suggestions for someone to evaluate?