Traditional software releases are relatively predictable.
AI releases are different.
Updating prompts.
Changing models.
Adjusting tools.
Adding memory.
Modifying policies.
Each change can influence how an AI system behaves.
Sometimes those changes are beneficial.
Sometimes they introduce regressions that only appear under specific conditions.
That’s why validation shouldn’t stop once development ends.
It should become part of every release.
Every deployment is an opportunity to ask:
What changed?
Did behavior improve?
Did reliability improve?
Did security improve?
If you can’t answer those questions, you’re deploying blind.
That’s why we’re building Crucible.
Helping engineering teams compare behavior across releases with confidence.
Pytest for AI Agents.
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