James Reggio

speaker

667 appearances 1 recordings 1 series first heard Jan 2026 last heard 17 Jan

James Reggio’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.

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Latent Space: The AI Engineer Podcast · Brex’s AI Hail Mary — With CTO James Reggio · 17 Jan 2026 podcast

Good.

Jug reptile.

Yeah, no, we're we're huge fans.

They're they've built something really impressive.

And I think the thing that constantly blows my mind about it is um

the way that they're able to just have a really impressive signal to to noise ratio.

Like the the comments that it leaves are very

Very high signal.

Uh like never I never regret going through all like sixty five comments it leaves on my on my diffs because it it catches so many things.

Yeah.

When we when we started building this this new agent um code base, like 'cause as we was saying, like it we were answering the question, what would you do if you built uh you know, a Brex disruptor today?

And it's like it wouldn't be to pick Kotlin and Elixir as the back end and uh and so we actually went with the full like TypeScript stack and and we we were building on all like public interfaces and um really trying to make sure that this agent layer was uh

Like arm's length from from the the good and the bad of of the core of our product.

And um and one thing I think what we did early on, and I don't actually know if this is true because again the team keeps sort of iterating, uh, but we we're having good uh good luck using um clawed code like in a GitHub action to basically go and do uh do more of that danger style like code review.

So have a uh a prompt for it that went through all of the different

facets that were more conceptual versus like rigidly enforceable by a linter and have it leave a big comment at the end with uh you know your conformance to the idiomatic coding patterns of the of the new repo.

Is that we uh we believed at the beginning that using RL for credit decisions would actually be a like would be the way that we would end up, or like credit and underwriting, like how much of a of a limit should we give to this business?

Um, that reinforcement learning would be the way that we would go about um building a model that effectively would decision.

In the way that um a human underwriter would.

And it turns out that it was, we made this big investment.

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