Sema AI review comments: blocking gate or advisory input for a repeatable team review process?

Our team is adopting Sema's AI-assisted code review to make feedback more consistent across pull requests and reviewers. The documented capability we are relying on is AI-generated review comments that draw on repository context, and we can run them either as autonomous output on every PR or as drafts that a human reviewer approves or edits.

The unresolved decision is how these comments should factor into our merge policy. Making them blocking (must be resolved before merge) maximizes uniformity but risks gating merges on confidently wrong suggestions. Keeping them advisory preserves human accountability but makes consistency depend on each reviewer's discipline. We also understand that identical changes may receive different feedback after model or configuration updates, which weakens strict repeatability unless versions can be pinned.

Assume a current Sema version; exact configuration options should be verified against the vendor's documentation.

Which mode have teams found works better as a documented policy: blocking or advisory, and why? If advisory, how do you keep feedback uniform across reviewers? And can model or configuration versions be pinned so equivalent PRs receive equivalent feedback?