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?
3 Answers
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.
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. “The model generated it, so I don't know how it works” shouldn't be an acceptable justification except for trivial, disposable scripts.
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 “done.”
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