OPEN LINK ↗
// 1h agoPRODUCT UPDATE
Macroscope Approvability Dashboard Makes AI Approvals Measurable
Macroscope’s new dashboard shows which pull requests its Approvability agent approves, escalates, or skips, helping teams understand approval behavior and measure how policy changes affect the results. Teams can tune rules for protected paths, routine changes, correctness thresholds, and required checks.
// ANALYSIS
This is the observability layer AI approval systems need: teams can calibrate automation against real review patterns instead of trusting an opaque percentage.
- –Breaks approval decisions into actionable verdicts, including Approved, Would Approve, Needs Human Approval, and Skipped
- –Makes policy tuning measurable through controls such as `alwaysApprove`, `neverApprove`, and `onlyApprove`
- –Keeps high-risk areas like authentication, infrastructure, and review configuration protected by default
- –Connects eligibility decisions with correctness findings, CODEOWNERS, prerequisite checks, and GitHub branch protection
- –At 30 agent credits per eligibility run, teams should monitor whether approval-rate gains justify evaluation costs
// TAGS
macroscopecode-reviewevaluationguardrailsdevtoolautomation
DISCOVERED
1h ago
2026-10-22
PUBLISHED
1h ago
2026-10-22
RELEVANCE
8/ 10
AUTHOR
Macroscope
