| 1 |
Cycle completion rate |
% of issues scheduled for a cycle that got closed by cycle end |
AI-Weighted Deliverable Completion (% against verified commits & tests) |
| 2 |
Throughput |
Count of issues closed per cycle / week / day |
Verified Task Output per Day (backed by Git SHAs & PRs) |
| 3 |
Velocity |
Sum of story-point estimates closed per cycle (if teams estimate) |
3-Way Signed Hours & Dynamic AI Weights (0–100 Daily KPI) |
| 4 |
Scope added / removed mid-cycle |
Issues added or deferred after the cycle starts (sprint-churn) |
Autonomous Daily Scope Rebalancing via AI To-Do Arrangement |
| 5 |
Lead time |
Time from issue created → done |
Linear Issue Creation → PR Verified Lead Time Telemetry |
| 6 |
Cycle time |
Time from started → done (excludes backlog wait) |
Active Working Window & Time Log Duration per Work Block |
| 7 |
Issue age |
How long each open issue has been sitting in its current state |
Stalled Deliverable Alerting & Priority Weight Escalation |
| 8 |
Workload per assignee |
Count / point-sum of open issues per person |
Daily Capacity vs. Assigned AI Weight Budget (8h / 100 pts) |
| 9 |
Burndown / burnup |
Remaining or completed scope plotted over cycle days |
Release Gate Readiness Burnup (Gate 1 QA → Gate 2 Defect → Gate 3 Demo) |
| 10 |
Triage queue depth |
Unassigned incoming issues waiting on triage |
Autonomous Issue Ingestion & AI-Triaged Priority Labeling |