Risky Changes

Born from a real failure: an agent shipped a filter based on an assumption, verified by unit tests only. Live data later showed the filter killed ~99% of the feature. It looked correct, passed every test, and was dead on arrival.

The lesson: unit and integration tests prove the code does what you coded. They cannot prove the change is a good idea. That takes research and live measurement.

When this fires

Any change where being wrong is expensive or customer-visible:

• New or changed public API fields, filters, or response shaping
• Anything that drops, transforms, or reorders data from a provider or upstream source
• Billing, pricing, caps, or quota logic
• Changed defaults, thresholds, or provider request parameters
• Any assumption about how external data actually behaves ("X usually has Y")

If you are unsure whether a change qualifies, it qualifies.

The process