How can a DevOps team use AI tools without getting stuck in endless review loops?
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MellowPine42
Our team has started using Claude Code and other AI tools to develop Terraform-based infrastructure on AWS, including EKS, Aurora, GitOps with Argo CD, CloudFront, API Gateway, and S3. We write tasks with acceptance criteria, but the generated changes still sometimes miss those requirements. When one AI reviews a merge request, another finds additional issues, and asking the first tool to fix those issues often produces yet another round of findings. The review process feels endless. How are other teams organizing AI-assisted development and reviews so that quality improves without relying on an infinite chain of agent feedback?
Answers
Multiple independent reviewers can be useful for catching serious problems, but they should not automatically become a chain where every new comment triggers another review. Decide in advance which findings block a merge, which require a follow-up ticket, and which are merely suggestions. That gives the team a consistent quality bar instead of allowing each model to redefine โdone.โ
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Treat the AI like another tool in the engineer's toolbox, not as the owner of the change. The person submitting the code still needs to understand it, explain the design, and remain accountable for the result. โThe model generated it, so I don't know how it worksโ shouldn't be an acceptable justification except for trivial, disposable scripts.
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Make the acceptance criteria executable or at least objectively checkable wherever possible. Review the specification before implementation, then give the agent the relevant Terraform provider and AWS documentation through suitable tools or references. Run automated validation and security checks in CI before spending human review time. A clear planning and specification phase usually helps more than adding another coding agent.
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The endless loop happens because open-ended prompts such as โfind any other issuesโ don't define a stopping point. Use a fixed checklist tied directly to the acceptance criteria, infrastructure tests, policy checks, and agreed security requirements. Have one review pass against that checklist, then let a human make the ship-or-no-ship decision. Once the required bar is met, stop chasing stylistic or increasingly speculative findings.
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