Software engineering became more reliable because teams agreed on standards.
Code reviews.
Automated tests.
CI/CD.
Static analysis.
These practices reduced uncertainty and improved quality.
AI engineering is reaching a similar point.
As AI agents become part of production systems, organizations need a consistent way to evaluate security before deployment.
That doesn't mean every AI system will be identical.
It means every AI system should meet a minimum security standard.
A baseline might include:
Prompt injection testing
Tool security validation
Behavioral consistency checks
Memory safety evaluation
Multi-turn attack simulation
The exact implementation will evolve over time.
But the principle is already clear.
AI security shouldn't start from zero with every project.
It should start from a baseline.
That's the engineering philosophy behind Crucible.
Helping teams create repeatable, measurable AI security practices.
Pytest for AI Agents.
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