Kyle Corbitt
speaker
658 appearances
1 recordings
1 series
first heard Oct 2025
last heard 16 Oct
Kyle Corbitt’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Oct 2025 with 1.
Appearances
So I talk to a lot of engineers at our customers and they're trying to deploy agents.
And it's so easy to get the initial prototype and like something that like kind of works well.
It is so hard to get from that to something that like you are confident is reliable enough to actually deploy in production.
And when you actually look at what those failure modes look like, it's like, oh yeah, like we know if it gets in this situation or if it gets like these kind of like inputs, like it behaves funnily, but then it's like, yeah, you can update your prompt to to address that, but like that's not scalable because at a certain point it's like gonna start breaking other things.
You know, you don't know what it's breaking.
You really want some way to just like say, okay, look, this thing you did there, that was the wrong thing.
Just like adjust this behavior when you get in this.
And then you know otherwise carry on, right?
And that's what we can do with RL.
And that's what we can do with continual learning.
It's like we don't have to like have this concept of like, oh, up front, I'm like trying to make the perfect model that solves everything.
It's like I'm trying to make a model that's good enough.
I can deploy it in production.
And then when these errors come in, I'm going to say, Oh, you know, exactly the I mean, very analogous to how you train a human employee.
Like be like, oh no, actually that's not what you should do in that situation.
All right, fix that and carry on.
And
that's just gonna make this whole process so much easier and I think that you know like I think that there is today like
10 times as much AI inference that could exist than is existing right now, just purely with projects that are like sitting in the proof of concept stage and have not been deployed.
Because there's like a huge bucket of those.
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