A product claiming to flag thousands of potential bugs can still create more engineering work than it saves. Review quality is a decision-support problem: a finding is valuable only if it points to a real defect or a justified risk, and it must be delivered before merging without burying the author in generic nits.
What the official product can and cannot do
Graphite’s documentation describes dashboard measures such as issues identified, accepted suggestions, PRs reviewed and downvote rate. Greptile describes a review agent that adapts to team feedback. Those metrics are useful operational telemetry, but acceptance is not an independent truth label: an author may accept an incorrect fix or reject a correct warning for schedule reasons.
A practical evaluation workflow
Create a blinded sample of PRs with a post-merge defect history and independent reviewer labels. For each AI finding, score reproducibility, severity, novelty, relevance and suggested-fix correctness. Record false positives, duplicates, missed known defects, review delay, engineer response time and final impact. Cluster repeated nits so a model cannot inflate its score by making five comments about the same underlying issue.
Limitations and historical pitfalls
Low downvote rates are not proof of high precision; many developers never provide feedback. The test set may overrepresent common web languages while ignoring domain-specific systems. Normalizing against PR size, code ownership and baseline bug rate is necessary before comparing teams or providers. A single retrospective month cannot prove long-run outcomes.
Data handling and permission boundary
Do not upload confidential PR archives to an unapproved comparison service. Remove secrets and personal data from historical samples, and secure reviewer annotations. Keep the AI’s own explanations separate from human ground-truth decisions to reduce confirmation bias.
Who should use this approach
Most valuable when engineering leaders already have enough code review volume to measure a baseline. Smaller teams can begin with a handful of labeled PRs and qualitative postmortems rather than invented statistics.
Alternatives and trade-offs
Combine AI review with deterministic linters, tests, code-owner review and production bug tracking; assess incremental benefit over those existing controls.
Version, tier, region and evidence status
This article is an editorial snapshot checked against first-party material on 2026-10-25. Version or historical applicability: Graphite metric definitions checked 2026-10-25. The exact software build in a reader’s organization, eligibility by country, enterprise agreement, separate provider model rates, code retention and full regional list have not been validated; such fields remain unknown rather than being guessed. Product statements are attributed to first-party descriptions, not independent hands-on tests. License guarantees, prices and seat limits require AI 10 dated snapshots or signed terms. Unverified details do not become timeless facts because they appear in current online documentation.
Deployment acceptance checklist
- Write the intended job and acceptance criteria; record the installed client release, plugin publisher, connected model/provider and tenant.
- Use a consented test repository with zero real secrets; capture installation permissions, processor regions, Git App grants and available audit logs.
- Verify the task boundary with a test that should fail when unauthorized: blocked file, network host or protected branch.
- Review proposed changes or comments against known requirements; run regression, security and integration checks independently of the agent.
- Document the owner, offboarding procedure, rollback limits and whether a formal procurement/privacy sign-off is still pending.
Sources & Further Reading
- https://graphite.com/docs/ai-review-comments (official technical material; undated/living source; checked 2026-10-25)
- https://graphite.com/docs/ai-reviews-setup (official technical material; undated/living source; checked 2026-10-25)
- https://www.greptile.com/docs/introduction (official technical material; undated/living source; checked 2026-10-25)