Why Kaser R&D Workforce keeps Linear's UX primitives (Teams, Cycles, Projects, Tasks, Workflow States) while replacing the human-scored measurement layer with autonomous AI telemetry.
| # | Linear Metric | What It Measures | Kaser Autonomous R&D Evolution |
|---|---|---|---|
| 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 |