Mihail Eric discusses AI-native engineering at Stanford (Part 4)

Being like, well actually that's not even the biggest problem that we have. So you take it however you like. Yeah. You mean like specifically for like Monaco or just like broadly for like organizations thinking about this broadly, because that is our audience. Yeah, I mean I think that it again, it's a function of how your scale. Right. And I think that for the enterprise there are a number of issues that aren't always. I almost feel like the answer is not even always, just more agents. And since we're still on the week five agent ready code bases, like when I talk to companies of like a certain scale that the things that are holding them back are really more just like low level, like DevOps CICD stuff. Right. They're like their merge queues are getting backed up. It takes too long to stand up an app because there's like actually more foundational things that have almost nothing to do with. They're like throwing in one more SRE bot or a la. A bot wouldn't be the answer to the problem because there's like more systemic things that they need to figure out. And so that brings us to weeks. Yeah, so that's where we get into. Yeah, exactly. Right. Yeah. There we go to the merge queue and beyond. Yeah. This is a love letter of sorts to modern Gentex code review. Yeah. I think that the biggest thing that maybe happened between last year and this year was just. It suddenly became very clear that code review is a bottleneck. Especially once people got really AI pilled and the velocity on generation became so high that you could in theory put up 30 plus PRs a day. And then what, like what's. At what point do you decide that you either have to institute yourself and put yourself in the middle as some way to. To gate and review things in the traditional way, but then you're, you're literally the constraint, you're the choke point of the velocity. And that's not good. Right. Then, then what was the point of generating at scale? Or you say, well, how do we maybe get the best of both worlds where we're instituting ourselves enough that we have trust in what's getting merged, but we're also able to kind of like merge at scale, but we're also not kind of free, freewheeling it here and anything goes. So a lot of. I think what I want to touch on here is just that, that middle ground, right. Which is like there are, there is a class of problems that if you've put in thoughtful design into your agent readiness, you've put in thoughtful design around your verifiability. There's a class of problems that you can feel comfortable, you know, some percentage of the time, essentially, like auto approving and what has to be true from a kind of a design perspective, from like an architecture perspective to just auto approve. And when do you have to insert yourself and say, actually, here, I still want to review this carefully. And even if you do review it carefully, are you still doing line by line review or are you doing something else? Right. So, yeah, some of this.