Key Features
The break happens when AI drafts at scale. Training + sampling are after-the-fact controls. By the time a bad commitment is found, the customer expectation already exists.
This is just moving the boundary from social enforcement to a hard system boundary for irreversible actions.
Curious if you’ve seen teams hit that inflection point yet.
The failure is architectural: once AI is allowed to draft at scale, “don’t feed it commitments” stops being a reliable control. Those patterns exist everywhere in historical data and live context.
At that point the question isn’t training, it’s where you draw the enforcement boundary for irreversible outcomes.
That’s the layer I’m testing.
Training governs what a model tends to say. Authority governs what is allowed to be acted on.
You can’t pre-block bad advice, but you can pre-block unapproved financial or contractual actions.
That’s the scope.
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