using AI agents. And there are prior there's different reasons why you would want to do this. And when we talk to customers, and we've talked to a lot of customers about this, uh the driving functions that are forcing them to adopt verification across all of their AI coding processes are are very similar, right? They want to make sure that AI code is verified consistently. They don't want to have different methods of verification applying to different projects or different teams. They want to have a standard rulebook that applies everywhere, no matter what tool is being used. They all also want to make sure they're using their AI tools effectively, right? Some of that a big part of this is token efficiency or just efficiency in general, but also just making sure that the tools are being used for the things they're being designed to do in ways that we know they're good at doing, right? So we talk a lot about token efficiency, and we talk a lot about making sure that the right models are used for the right projects, and so on. Finally, third, catching issues from a security standpoint as early as possible in development cycle. Shifting left on security issues has been very important for a number of years now, and now that AI is writing more and more code, catching security issues up front is extremely critical, especially now that we're in a world where CVEs are announced and then immediately exploited uh by bad actors almost often the same day. And so you need to make sure that your code is as hardened as possible from those types of issues creeping into production. And finally, maintaining compliance. Many of you, I'm sure, work in a regulated industry, uh and for those types of situations where you need to be able to prove that verification is run constantly and consistently across the board. Maintaining an audit trail that allows you to prove that is extremely important.