The first generation of AI products proved something remarkable.
Language models could solve real-world problems.
The next generation faces a different challenge.
How do we make those systems dependable?
As AI becomes part of production infrastructure, engineering becomes more important than experimentation.
Teams need repeatable testing.
Continuous validation.
Behavioral observability.
Security evaluation.
Reliable deployment workflows.
These aren't prompt engineering problems.
They're software engineering problems.
The organizations that succeed over the next decade won't necessarily be the ones with access to the largest models.
They'll be the ones that build the strongest engineering systems around those models.
That's the shift we're seeing today.
And it's one of the reasons we're building Crucible.
Helping engineering teams bring discipline, security, and confidence to AI agents.
Pytest for AI Agents.
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