Originally published on AI Tech Connect.
What a silent default swap actually breaks The instinctive worry, when a vendor announces that the model inside your coding agent is changing, is about quality. Will the new one be worse? That is the wrong question, and it is wrong in a way that leads teams to do nothing until the change has already landed. What actually breaks is calibration. Over months of daily use, a team builds an unwritten model of how its coding agent behaves. You learn roughly how much it over-edits, so you learn how large a diff to expect from a two-line ask and when a large diff means the model misread the request. You learn whether it respects the conventions file in your repository root and how firmly you have to phrase a rule before it sticks. You learn its tool-call reliability — whether it retries a failed…
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