progressive delivery

The loop is easy; the statistics are not. This question finds engineers who can size a traffic slice against sample noise and name the false rollback that kills trust in automation.

Compare rollout capacity, traffic exposure and state compatibility, then work through a payments canary with too little evidence to promote.

Deployment and release are two decisions, not one. Choose stable cohorts, test failure defaults and propagation, and retire temporary flags without breaking older application versions.

Rings, SLI gates and Git-expressed promotion: sync waves order resources inside a cluster, but fleet sequencing is an automation you design on top.

For scheduled and stateful workloads the exposure unit is records and time, not requests: shadow runs on real input, row-level diff budgets, checkpoint compatibility, one-cycle warm standby.

The automation stopped because it could not decide, which is the correct behaviour and an incomplete design. Somebody has to decide, and the interesting question is what evidence that decision needs.

Comparing the canary against last week is how a Tuesday afternoon looks like a regression. Compare it against a baseline running the same traffic at the same moment, and decide with a threshold somebody can defend.