I finally tried this an about a year so after it was first posted. Due to bugs in our toolchain we had a run book for hot fixes that was about twice as complic

I finally tried this an about a year so after it was first posted. Due to bugs in our toolchain we had a run book for hot fixes that was about twice as complicated as the normal release process. I never got the credit it deserved but it went from people only using it for sev 1 issues and “last mile” work in epics that were winding up, say once every ten weeks, to using it on average once a week and a couple times 3 in one week. We were able to dig a lot deeper into tech debt because not every single thing had to be a feature toggle. If you’re at a small company where cloning prod data into preprod is easy, you won’t see this sort of result. But I counted over 150 endpoints we talked to and I believe that averaged 3 per service. So that’s a lot of datasets, and some of them ingested in a Kafka-before-Kafka-existed sort of manner. We had only one guy who would even try to clone prod data, he only had the time (and really energy) to do it once or twice a year, and that was much slower than our customers and features morphed. So it was down to fiddling with the blue-green deployment process and jmeter to figure out if we were close and how to measure success/failure before we went live. And in the end it was the fiddly error-prone build process that stymied people until I half-automated it. Later on as we ramped up its use I hunted down all of the URLs for the manual steps and put them in a lookup table in the tool, and ended up exposing them for the normal validation process we did for release sign off as well. Which made that process a little faster and less stressful for the coordinator (that process was annoying enough that we round robined it through three separate teams to share the load)