Jira Metrics

Anatomy of a ticket

Range:
Validation & drill-down view. Use this page to understand exactly how each flow metric is computed for one project, and where the AI cohort differs from the human cohort. Headline numbers on /flow and /overview roll up the same formulas across all projects. Hover any lane label for the formula and a sample-size note.

Anatomy of a ticket — TECH

Window: 30d · All 8 · AI 0 · Human 8

Jira Flow Lead Time (raw / wall-clock)
154hn=8AIno dataH154hn=8 · directional
Lead Time (active, ÷ pauses)
106hn=8AIno dataH106hn=8 · directional
Time to Ready
2.5hn=6AIno dataH2.5hn=6 · directional
Cycle Time (raw / wall-clock)
165hn=6AIno dataH165hn=6 · directional
Lead Time for Changes
Not enough data in this window
Cycle Time (active, ÷ pauses)
117hn=6AIno dataH117hn=6 · directional
Time to First PR
69hn=1AIno dataH69hn=1 · directional
PR Review Duration
Not enough data in this window
Created
Ready
In Progress
In Review
Done
Deployed

AI vs Human comparison

Bars proportional within each row (max = 100%). Δ% lower-is-better — green = AI faster.

No lane has both AI and Human samples in this window. Try a longer range, or check that this project has AI-assigned tickets.

Not measurable (one cohort has zero samples):

  • ·Jira Flow Lead Time (raw / wall-clock) — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·Lead Time (active, ÷ pauses) — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·Time to Ready — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·Cycle Time (raw / wall-clock) — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·Cycle Time (active, ÷ pauses) — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·Time to First PR — no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • ·PR Review Duration — no tickets / PRs measurable in this window
  • ·Lead Time for Changes — no tickets / PRs measurable in this window

Cohort breakdown

âš  marker when AI or Human cohort sample size is below 10.

MetricAll p50nAI p50nHuman p50nΔ (AI vs Human)
Jira Flow Lead Time (raw / wall-clock)154h8—0 ⚠154h8 ⚠—
Lead Time (active, ÷ pauses)106h8—0 ⚠106h8 ⚠—
Time to Ready2.5h6—0 ⚠2.5h6 ⚠—
Cycle Time (raw / wall-clock)165h6—0 ⚠165h6 ⚠—
Cycle Time (active, ÷ pauses)117h6—0 ⚠117h6 ⚠—
Time to First PR69h1—0 ⚠69h1 ⚠—
PR Review Duration—0—0 ⚠—0 ⚠—
Lead Time for Changes—0—0 ⚠—0 ⚠—

All = every measurable ticket / PR in the window. AI = agent-owned cohort (ticket assignee or PR author is an active agent at completion). H = human cohort (everyone else, including unassigned). Δ compares AI vs Human p50 — negative = AI faster.

Side metrics

Throughput / week
1.9
AI 0.0 · H 1.9
WIP — Active now
61
Blocked 11
Bug rate / week
0.0
no bugs in window
AI participation
0%
AI 0 / 8
Top bottleneck
Done
median 244.5d
Deploys / week
0.0
ADO-style
Change failure rate
—
no ADO mapping
PR acceptance
—
no PRs in window

🧠 AI deep analysis

Generate an LLM-driven explanation of WHY the AI cohort is faster / slower on each lane. Cites specific tickets from the evidence pack, suggests next steps.

Active variants use business hours minus pause statuses (same formula as the per-project Cycle / Lead pages). Raw variants are pure wall-clock. AI cohort = ticket assignee (or PR author) is an active agent at completion time. Sample-size suppression marks values directional only when AI or Human cohort drops below 10 — the value is still shown for context but trust it less.

1 auto-filtered zombie (1 direct · 1 old) — Rules