Kwasi Ankomah

Like, too much time, right?

How do I scale this to be something that is as reliable and as consistent

as a normal AI application.

That's where a lot of, that is, yeah.

It's a great question.

There's a couple of things that we need to think about.

Firstly, the memory is a big one.

How do we ensure that the agent is learning over time?

We need to make it more flexible in its learning.

And that in itself is a whole area.

What memory do you attach it to?

How much do you keep?

And also, where it gets to a point where the agent has too many memory and too many tools and gets confused.

Those are probably big things in terms of memory.

But one of the things that I think about a lot is in terms of the tool calling and the guardrails around that.

One

one of the big things with agents was let's give them all the tools, right?

And now we've got to a point where we can give them all the tools, but as anyone who's built agents in production will find, you give your agent all the tools, it might have a problem picking which tools it's got, yours should use.

And people figuring out that balance for their, between building like a generalist agent that does everything

So like an agent that basically manages your call center is going to be a really big shift.