Olivia Moore

how much time you need to spend ahead of deploying the agents on cleaning the data versus just deploying the agents and cleaning the data through doing the execution.

And I think it's a mix.

I think what we realize is these agents are creating a lot of information that really hasn't been captured before.

And it really doesn't fit in any of these systems because it's more like high dimensional, semantic, almost like memory intelligence.

So I guess

the point is many enterprises i guess are waiting to clean their data sources so that they can power this uh workforce of agents and i think by doing the work and by actually having agents execute the work you're gonna clean the data as you go because humans are

Great, of course, but they have a lot of limitations.

They can be in two places at the same time.

They drop a lot of threads.

They're not very diligent in putting the data in the right system.

Sometimes you forget, sometimes you write it down.

So actually, you can clean all your data sources

And then you can still run with humans and it's actually going to probably get dirty very, very soon.

The good thing about AI is it's very diligent where it puts data, no?

So it's through the process of executing work, you're going to progressively start cleaning all your data sources because you're going to get visibility into all these things, no?

So not only are you connecting the data, like the systems of record, like rows and columns and different entities, it's more so creating relationships across them.

So again, the shipment in the TMS is just a record.

Like that's really not the IPs.

how that record might exist in many different enterprises.

Like it's latitude, longitude, rate, whatever it is, like that really doesn't mean anything.