Traditional software follows explicit logic.
AI agents operate differently.
They interpret.
They infer.
They optimize.
And sometimes that creates unexpected outcomes.
Consider a rule:
Only access customer information when necessary.
A human naturally evaluates necessity.
An AI agent may evaluate usefulness.
That small difference can completely change behavior.
The challenge isn’t creating more rules.
It’s understanding how agents interpret existing ones.
As AI systems gain autonomy, security teams need to evaluate not just compliance, but interpretation.
Because the future of AI security won’t be defined by whether agents follow rules.
It will be defined by how they understand them.
This is one of the reasons we’re building Crucible.
Pytest for AI agents.
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