So every background agent, you know, there's a a few different principles that we like to think about with our background agents. When does it work? How does it work? How does it know what to go do? When does the agent work? It can work in a bunch of different ways. It can just do it on a schedule. Maybe this is the the report, etc. Just kind of do some summarization for me kind of on an ongoing basis. Maybe it's a weekly event, right? We do an on-call handover uh every Thursday, and so that a lot of um the work that our agent does is sort of prepare like what what are the kind of interesting trends from the last week that the next on-caller needs to sort of understand as they pick up the rotation. Um, event streams, so you know, there's lots of systems that will sort of push events as kind of key things happen. Um, so deployments go through a CI ICD pipeline. Um, there's other sort of uh Slack-based, right? We get a lot of uh Slack things messages coming through, um etc. Um, and these are things that we can sort of pick up and trigger and say, "Oh, if this event happens, um let me sort of understand what that event is and go do some work." Um, and then message-based, so I can just tell it, "Hey, go do some work." And it will go do some work. That's fantastic. Um, how does it run? Always runs. It's in the cloud. Um, so if you close your laptop, it's okay. Um, runs inside of a sandbox, so it has kind of a file system underneath it. Um, this allows it to sort of self-organize a lot of its work, etc. as it's doing uh doing things. Um, and then obviously back to the learning loop, right? So, that idea of knowledge and sort of a memory system underneath that um to really understand your systems and as it's doing a task, able to sort of reflect on that task and uh you know, do a better job next time. Or the things that it learned from one task, it can sort of apply into a different task. Um, because again, this this sort of shared uh sort of knowledge system um works across all the different tasks that we have.