Aglet

Learn from a recurring post-release error spike

A repeated spike is an opportunity to improve the release loop, not a reason to blame the last deploy by timing alone. Compare prior occurrences and capture what was visible, what arrived late, and which evidence finally separated a release regression from background noise.

Keep the lesson for the next incident

  1. Compare prior occurrences

    Link earlier spikes by path, environment, release stage, dependency, or symptom shape. Note what repeated and what changed between incidents. If matching is approximate, label the relationship as a hypothesis and preserve the differences.

  2. Record the missing early clue

    Describe the first decision that was delayed: missing commit context, weak ownership, sparse examples, or a late downstream metric. Identify the smallest durable context that would have shortened that decision without creating a noisy alert.

  3. Define a future trigger

    Choose a recurrence condition tied to the original path and impact, such as the same error class after a release boundary. State who reviews it, what evidence they collect, and when the lesson should be reassessed.

What to carry forward

The lesson should produce one concrete change to release context, observability, or review practice plus a recurrence rule. Keep the lesson honest about uncertainty and avoid claiming prevention until a later release demonstrates that the new evidence arrives in time.

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.

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