There is a persistent belief in AI engineering:
If the model were smarter, agents wouldn’t fail like this.
In practice, the opposite is often true.
Stronger models do not respect boundaries better.
They simply cross boundaries more gracefully.
As models improve, several things happen:
Hallucinations become more coherent
Assumptions are better justified
Errors are wrapped in confident explanations
The system sounds correct even when it is wrong.
This creates a dangerous illusion of reliability.
When an agent “runs wild” with a weak model, mistakes are obvious.
When it runs wild with a strong model, mistakes look intentional.
This is not progress.
It is risk amplification.
Safety does not come from better reasoning alone.
It comes from removing authority from the model.
A model should never decide:
when execution starts
when it continues
when it is acceptable to proceed
Those decisions belong to the system, not the generator.
Until that separation exists, improving model capability only increases the blast radius of failure.
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