Architect Interaction Patterns

A public extension to the Azure SDK Automation impact dashboard.
Evidence through: Oct 18, 2026 Refreshed: Oct 18, 2026 Period: Cumulative since Sep 22, 2026

Back to the main impact dashboard

How to use this view: look for workload, aging, and review-flow patterns that suggest where automation, clearer context, or better balancing could reduce waiting. Elapsed time is not a measure of an architect's effort or performance.

Observed interaction summary

13
individual assignments observed
5
signoffs attributed
4
open assigned reviews
3
completed assignment-to-signoff samples

Per-architect interaction data

GitHub handles are shown as plain text. Assignment and signoff are separate interactions, and the person applying signoff may differ from the assigned reviewer. Small sample sizes can move medians sharply, so compare the sample count with every timing value.

GitHub handle Assigned Signoffs Open assigned Completed samples P50 assignment to signoff P50 request to signoff Oldest open
heaths 1 3 0 0 Unavailable 2.4 hours Unavailable
JoshLove-msft 1 1 0 0 Unavailable 30.7 minutes Unavailable
helen229 0 1 0 0 Unavailable 6.8 minutes Unavailable
jeremymeng 1 0 1 0 Unavailable Unavailable 9.0 days
jhendrixMSFT 1 0 1 0 Unavailable Unavailable 8.8 days
JonathanGiles 1 0 0 1 1.4 days Unavailable Unavailable
kashifkhan 1 0 0 0 Unavailable Unavailable Unavailable
LarryOsterman 1 0 1 0 Unavailable Unavailable 8.9 days
m-nash 1 0 0 0 Unavailable Unavailable Unavailable
maorleger 1 0 0 0 Unavailable Unavailable Unavailable
qiaozha 1 0 0 0 Unavailable Unavailable Unavailable
samvaity 1 0 1 0 Unavailable Unavailable 2.0 days
tjprescott 1 0 0 1 1.4 hours Unavailable Unavailable
xirzec 1 0 0 1 30.6 minutes Unavailable Unavailable

What can be learned

Workload distribution

Assignments and open work show whether review demand is concentrated and where balancing or additional coverage may help.

Waiting patterns

Assignment-to-signoff timing can identify review paths worth examining, but it includes service-team responses, PR readiness, and review complexity.

Automation opportunities

Repeatedly slow paths can be inspected for missing context, manual coordination, or routine checks that automation could prepare earlier.

Evidence limits

GitHub events measure workflow elapsed time, not cognitive effort. Raw events and pull-request identifiers remain outside the public site.