Arya

Everyone talks about building AI agents. Far fewer talk about what happens after they're deployed.

In your experience, what's usually the first thing to go wrong?

• Infinite loops?

• Tool misuse?

• Hallucinated actions?

• Rising API costs?

• Poor observability?

• Something else entirely?

We're building Clyro around runtime governance, but I'm curious what problems other teams are actually seeing in production. I'd love to hear real stories and lessons learned.

Building with AI feels magical... until it doesn't.

You ship fast, users love it, and everything seems to be working perfectly.

Then one day, an unexpected behavior breaks a critical workflow, and you realize you had no monitoring, evaluation, or fallback mechanisms in place.

Every month, we all contribute more to AI through writing, responding, conducting research, making reservations and even paying money. I now fully trust some of it. There are still parts of it that make me want to stay behind the wheel.

I would like to know where your line is. Which task would you gladly let AI complete on its own, and which would you never let it do without first verifying? There are no wrong replies; it could be personal or professional.

Since we are developing Clyro for just that space between "let it run" and "but keep it in check," I would love to know where people draw the line.