AI Task Model Switching Needs Concurrency Contracts to Prevent Race Conditions

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Switching an AI model mid-task is a multi-step distributed operation, not a simple settings change, making it vulnerable to race conditions when two switch requests overlap. If two requests complete out of order, a later-arriving response can silently overwrite the user's most recent intent, leaving the wrong model active. A generation-based guard addresses this by assigning an incrementing generation number to each switch request and only applying a completion when its generation matches the task's latest requested generation. MonkeyCode's commit c58bcd4 logs switch attempts with metadata including request IDs and timestamps, but no explicit compare-and-swap or per-task serialization contract has been identified in the reviewed code. Developers are advised to define clear contracts for duplicate requests, competing requests, late successes, and crash recovery, and to back them with targeted concurrency tests.

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