Here's a pattern I keep seeing with SaaS teams shipping AI features:

The agent works great in dev. Demo goes well. Stakeholders are hyped. You push to production and... silence. No errors. No crashes.

But two weeks later, your support queue is full of "the AI gave me wrong info" tickets.

*The problem isn't that agents break. It's that they break quietly.*

Unlike traditional software, AI agents don't throw clean errors. They hallucinate with confidence. They subtly drift. They handle edge case #47 differently on Tuesday than they did on Monday.

What actually works:

• Regression testing with real conversation flows, not just unit tests on individual prompts

• Output validation against ground truth — does the agent's response actually match what it should say?

• Behavioral drift monitoring — comparing agent responses over time to catch when quality degrades

• Adversarial probing — intentionally trying to break your agent before your users do

We built Vanica because we kept solving this same problem from scratch at every company. Automated QA for AI agents shouldn't require a PhD and a custom infra team.

If you're shipping AI in your product, test it like it matters — because your users will.