/self
The Weekly Observability Reset
A small weekly ritual that keeps my AI workflows honest after launch.
I used to debug only when things broke. Now I review behavior before trust drifts.
Every week, I pick one workflow and replay it from logs, not memory. I check what the model proposed, what the runtime allowed, and what evidence marked completion. The goal is simple: find one silent assumption before it becomes production folklore.
This ritual is less glamorous than shipping features, but it is where quality compounds. Systems decay in small increments, and a weekly reset catches those increments while they are still cheap to fix.
I also write one line after each review: “what signal would have told me earlier?” That question keeps my attention on instrumentation, not blame. Over time, it has changed how I design flows. I now ask for observability at design time instead of bolting it on after incidents.
The reset takes twenty minutes. The confidence it creates lasts all week.