Keep the lesson for the next incident
Compare demand and decision shape
Review which item types reached the reviewer, how many were duplicates or underspecified, and which required unique expertise. Tie the lesson to observed queue behavior rather than a general claim about workload.
Assess redistribution results
Compare review evidence, escalations, reopenings, and waiting outcomes before and after the change. Distinguish faster routing from better decisions and note cases where delegation was correctly rejected. Compare decision quality signals with queue speed so the lesson does not reward shallow review.
Improve one queue practice
Adopt one adjustment such as a question template, expertise map, review window, duplicate filter, or completion signal. Assign an owner and a future queue observation that can test the adjustment’s limits.
What to carry forward
The learning record connects overload to its actual queue pattern, redistribution result, and one testable review improvement. Stop when the adjustment has an owner and evidence plan; keep scarce expertise boundaries explicit rather than optimizing only for speed. Keep scarce expertise visible as a boundary in the next capacity review.
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