Operations | Monitoring | ITSM | DevOps | Cloud

It's 2:14am. A pager alert wakes an on-call engineer for a checkout failure hitting a slice of customers. By the time they've pulled the right logs, cross-checked the deploy history, and finally gotten the bug to happen again in front of them, the sun's coming up. The incident report will list two hours of downtime. It won't say anything about the day that engineer just lost, or the fact that nobody on your team could have told you in advance how long that reproduction step was going to take.

The regression nobody catches passes a busy review and ships. An off-by-one, a change that reads as sensible and quietly breaks something, gets a nod from a tired reviewer and lands in production, where it erodes trust one small defect at a time. You can have an AI code reviewer running on your own repository in the time it takes to read this page. Get started without having to book a demo or contact sales.

There's a specific, recognizable point where a team's use of AI agents changes shape. Not when they adopt agents; most teams already have. It's when agents stop running on someone's laptop and start running on infrastructure that the whole team can see. This is a real technical shift, not a policy change or a maturity score. Here's specifically what's different on each side of it.

Most of what people mean when they ask "how does this actually work" comes down to three words used precisely: workflow, run, and gate. This is what each one is, and how they fit together.