A feature can work exactly as demonstrated and still be an unsafe foundation.
The trap is mistaking interface convenience for an execution contract.
If a workflow depends on an instruction surviving inside an active AI session, the important questions are no longer whether the model understands the task. They are:
Where does the schedule live?
What state must persist?
What makes execution observable?
What happens when the session ends, the surface changes, or the provider changes the behaviour?
Most teams test the happy path: did it run?
Architecture starts one layer lower: what guarantees made it run, and are those guarantees durable enough to depend on?
That distinction matters because convenient primitives become invisible dependencies very quickly. Once people build operations around them, portability gets expensive and failures become difficult to diagnose.
The real risk is not that the feature fails today.
It is that it succeeds well enough to become infrastructure before anyone establishes whether it deserves to be.
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