Overview
OpenCodeReview turns a review request into a policy-filtered, bounded AI review: resolved inputs, explicit rules, staged context, resource limits, grounded findings, persistent coverage, and visible publication outcomes. The series shows how these boundaries support governability and a precision-oriented operating point without proving completeness; human approval remains essential.
Learning outcomes
- Deterministic Git, selection, and rule layers make the review’s evidence boundary inspectable.
- Metadata grouping and staged tools add cross-file context without granting an unbounded repository assignment.
- Budgets and lifecycle records distinguish completed, partial, failed, and not-reviewed work.
- Excerpt-based anchoring and publication routing preserve uncertainty when an inline location cannot be proved.
- The design exposes precision–recall and cost trade-offs; human approval remains necessary for consequential decisions.
Keywords
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PlansFAQ
What does OpenCodeReview actually review?
OpenCodeReview reviews a workspace, a selected commit, a branch-relative range, or whole files in scan mode. Workspace input can move; commit and range modes preserve historical identity, while scan mode does not attribute findings to a change.
Why does OpenCodeReview put deterministic controls around the model?
OpenCodeReview uses deterministic controls to keep file scope, rules, group limits, budgets, locations, and lifecycle outcomes stable and inspectable. The agent remains responsible for context retrieval and code interpretation inside those boundaries.
How can teams tell whether an OpenCodeReview run was complete?
Teams determine completeness from the sealed selected-file set and each item’s terminal outcome, not from comment count. A run is complete only when every selected item completes or is compatibly reused; failures, limits, or undispatched groups make it partial or failed.
Does OpenCodeReview guarantee that AI review finds every defect?
No. OpenCodeReview does not guarantee complete detection or correctness. Its first-party benchmark reports a precision–recall–cost trade-off with lower recall than the comparison setup, and the retained evidence is not an independent replication; human judgment remains necessary.
What remains a human decision after OpenCodeReview reports findings?
People still decide whether a finding is correct, whether a suggested fix fits the requirement, whether coverage is sufficient, and whether the change should be merged. OpenCodeReview is a review tool, not an autonomous merge authority.