this at Atlan. Era one and this was roughly about 18 months ago now. We started on the the track of bootstrapping agents. And the way we went about it was and we started this with our customer experience team and we did this jobs to be done analysis map, right? And so we said, "Hey, if you are someone on our customer experience team, what are all the things that you do on a day-to-day basis?" And then we made some hypothesis. We We said, you know, for example, one part of the job is documentation and meeting prep. Uh we said, "Well, AI could probably do that job pretty well." Uh and so we built the scaling factor. So, on the other hand, relationship management is something that our customer experience team does. And we said, "Hmm, that doesn't sound like something AI is going to be able to do anytime soon." And so we built the scaling factor. >> [snorts] >> And then we basically started bootstrapping these individual agents that were like built for that specific topic. Uh our team got creative, so we had Hermione, who is our health intelligence lead, and then we had, you know, MoneyPenny, who was our financial risk analyst, and we just made that particular agent really good at doing that one thing. Um and that worked for some time. Um but then we realized there were some challenges with this approach. The first, context engineering. Uh we got to the point by middle of last year where building an agent was really easy. Took like 5 minutes. Uh but giving it the business context that it took to actually get it to be accurate took forever. Uh quality of the agent often dependent uh on the quality of context engineering, and that led to a lot of weird lost trust cases with our stakeholders. >> [snorts]