The Effect Native Loop. So, originally we were on LangGraph and that was fine until the team really started to scale uh and our use cases started to evolve. So, we decided to move over to our own kind of Effect Native Agent Loop to have full regency over this uh Agent Loop such that if we have complex use cases or features that we need to build, we could kind of get in we we had full control of the of the Agent Loop. And not only that, but now we're fully on Effect. So, all the cool things you get with Effect is now propagated throughout the entire Agent Loop, like the tracing, structured concurrency, the logging, everything is more fine-grained control, and it it really allows us to really unlock the full potential uh having our own Agent Loop from the ground up. Um so, another thing I wanted to mention is on the left side, you'll see a code example. This is really the basics of the Effect Loop that we're using. Uh we're using this thing called the Effect AI package, and in that package, there's this thing called um there's a chat and a language model. So, with the chat, you can instantiate like an a chat, for example, and then you could stream text using um that that kind of stream text function, you could pass in a prompt. And what's cool is uh with a language model under the hood uh of since we're kind of doing dependency injection, we could pass in a different language model if we were to uh hot swap to another one, for example. So, really just having full control of our own Agent Loop just kind of gives us all the levers, and it really just unlocks the full capabilities of the model, and uh for the team as well, to have full agency over this this loop.