Keep the lesson for the next incident
Record the measured boundary and trigger
Write down which interval dominated, how it was measured, and what route or data shape exposed it. Include whether the behavior required a cold cache, a large account, a particular viewport, or a recent release. Link the baseline and experiment identifiers so the conclusion can be revisited.
Add a guard around the fragile assumption
Choose a durable check suited to the boundary: an asset-size review, a route timing measurement, a query-data limit, or a readiness smoke journey. Define the signal that should open work and the owner who interprets it. Keep the guard focused on the observed risk.
Schedule recurrence review after growth
Set a review point tied to a meaningful route, dataset, or release change. Re-run the same trace when payload shape, shared startup code, or first-action requirements change. If the old comparison no longer represents usage, replace it with a new representative case and document why.
What to carry forward
The learning record should preserve the first divergent stage, the conditions that revealed it, the accepted tradeoff, and the recurrence signal. It is useful when a future engineer can tell what to measure before optimizing and can distinguish a known budget breach from a new failure.
Keep the decision with the work.
Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.
Create an account See the product workflow