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 — EVO

Window: 30d · All 80 · AI 27 · Human 53

Jira Flow Lead Time (raw / wall-clock)

26.5dn=80AI169hn=27H33.9dn=53Δ -79%

Lead Time (active, ÷ pauses)

24.0dn=80AI169hn=27H33.1dn=53Δ -79%

Time to Ready

13.8dn=76AI43hn=23H19.0dn=53Δ -91%

Cycle Time (raw / wall-clock)

164hn=76AI147hn=23H169hn=53Δ -13%

Lead Time for Changes

Not enough data in this window

Cycle Time (active, ÷ pauses)

160hn=76AI147hn=23H166hn=53Δ -11%

Time to First PR

0.0hn=3AI0.0hn=1 · directionalH12hn=2 · 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.

Jira Flow Lead Time (raw / wall-clock)

Created → Done · wall-clock hours including weekends and pauses

-79% · AI fasterView tickets (27/53) →

169h n=27

Human

33.9d n=53

Lead Time (active, ÷ pauses)

Created → Done · business hours minus configured pause statuses

-79% · AI fasterView tickets (27/53) →

169h n=27

Human

33.1d n=53

Time to Ready

Created → first transition into a ready status (Selected / Approved / Ready)

-91% · AI fasterView tickets (23/53) →

43h n=23

Human

19.0d n=53

Cycle Time (raw / wall-clock)

First cycle-start status → Done · wall-clock hours

-13% · AI fasterView tickets (23/53) →

147h n=23

Human

169h n=53

Cycle Time (active, ÷ pauses)

First cycle-start status → Done · business hours minus pauses

-11% · AI fasterView tickets (23/53) →

147h n=23

Human

166h n=53

Time to First PR

First cycle-start → first PR opened · business hours

directionalView tickets (1/2) →

0.0h n=1 · dir.

Human

12h n=2 · dir.

Not measurable (one cohort has zero samples):

·PR Review Duration — no tickets / PRs measurable in this window
·Lead Time for Changes — no tickets / PRs measurable in this window

Cohort breakdown

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

Metric	All p50	n	AI p50	n	Human p50	n	Δ (AI vs Human)
Jira Flow Lead Time (raw / wall-clock)	26.5d	80	169h	27	33.9d	53	-79% (AI faster)
Lead Time (active, ÷ pauses)	24.0d	80	169h	27	33.1d	53	-79% (AI faster)
Time to Ready	13.8d	76	43h	23	19.0d	53	-91% (AI faster)
Cycle Time (raw / wall-clock)	164h	76	147h	23	169h	53	-13% (AI faster)
Cycle Time (active, ÷ pauses)	160h	76	147h	23	166h	53	-11% (AI faster)
Time to First PR	0.0h	3	0.0h	1 ⚠	12h	2 ⚠	—
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

18.7

AI 6.3 · H 12.4

WIP — Active now

Blocked 5

Bug rate / week

2.8

AI participation

34%

AI 27 / 80

Top bottleneck

On Hold

median 13.2d

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.

133 tickets excluded — managed centrally.