“There's two stages to winning the agents. The first is being picked by the model. And then the second is, if they get stuck, you don't want them to switch away. So guaranteed they'll get stuck at some point. You just want to give them the tools to remedy. … You do have to re-engineer a lot of what previously was just for humans and developers for agents.”

…the error messages, you are literally telling them how to fix this error or to go and read some docs to fix the error. So a lot of it is just picking your use case and just trying to do these loops on improving your own error messages so that you can one-shot basically anything that you want.

…makes sense. Basically, you know, if you're being used by an agent, the agent will encounter an error message. In the past, a human would have to go Google that and do a bunch of stuff. But here you can just say, oh, like you're just, you know, out of inode, so you should probably go do this. Yeah,

Do you test your project, your product for agents? Like, do you have agents like try to use Superbase and then report all the places where they had trouble?

Yeah, yeah, yeah. All the different models. We've got an eval's website where we see which models are building the best. And we've got our own eval suite internally. We eval as well which ones are best just with Postgres. So that's interesting for the industry and build skills around it so that…

Kerry, when you built G-Brain, you built it on top of Superbase. Talk about like, how did you decide to build it on top of Superbase? Well, how is it like for you to be a user?