Token Economy and AI Agents: The Final Hurdles in Enterprise Deployment

It's very much based off of your requirements, your

skills, your knowledge, your processes

that now need to be defined within

your AI stack. And that really is the last mile. And

I think that's even I saw that from both conferences where

the realization that there's still a lot of work that needs to be done to

get AI to a point where it's actually very fine

tuned, very functional, very efficient.

And right now it may work, but it may not be very efficient for scaling,

it may not be efficient for cost, it may not be efficient for the new

token economy that we're seeing. And the last mile is

historically the biggest problem to crack. Right. And once you solve that problem,

Amazon as a physical last mile in terms of

how they actually execute on delivery, right, because you can have

warehouses, but everybody lives in a different house, right?

So there's a lot of little last miles. It's, it's

death by a thousand paper cuts, if you will. Proof of concept

projects are everywhere. Real business value, that's

the hard part. This is data Driven.

I'm Frank Lavinia and with me I have a very special guest,

Christopher Newland, who is a colleague of mine at Red Hat. And

we're gonna do a deep dive. You, you travel all the time.

I know that I travel two weeks back to back and that was a been

a while since I had to do that. But it is conference

season and so you

were at IBM think last week

and you were also at Red Hat Summit this

past week, as was I. I have my Atlanta I heart I Red Hat

Atlanta T shirt on and

so how's it going, Christopher? But yeah, it was nice

because one of those two IBM think was

actually in my area of Boston, so I was able

to attend that locally. Still, still a lot

though. Like, you know, you're going in at like 7 in the morning to be,

try to beat traffic and then you're leaving at like 10 o' clock at night.

But it's very poor. Yeah,

yeah. And those conferences were very, very different, targeting very

different audiences. So it was, I felt like I got kind of

two perspectives of the AI world and

what people are concerned about. One from a very like executive

lens and another one from more the day to day users,

developers, engineers who are actually implementing the AI.

So which is which? I think I know the answer, but yeah. So

I IBM think is an executive conference.

So I think it's normally director level or above.

So it's targeting a lot of C suites, senior

directors, I think the

most that you would ever, lowest you would See would be like a senior manager

of some sort. But for the most part it's a C suite type of

conference. And a lot of the conversation

there is more about the business return of

AI and what does that look like this year. And then

Red Hat Summit is very much about

the system administrator, the cluster administrator, the

sre, the developer who's actually

utilizing these technologies and actually like implementing something

with it or managing something with it. So very like two

different lenses to the same challenge within the industry.

Yeah, no, it was interesting. And I don't know about you and what you,

the attendees this year had much better questions, I think, than any other

Red Hat event than I've ever AI questions than I ever seen before.

Right. It seems like people are struggling to

implement this in a way that is secure,

stable, scalable.

And I think we also have a much better platform story this year than we

had in previous years. Absolutely. So the way I've been

framing it to people, it kind of goes into two terms. So the first

term I've been using with people is that last mile.

And that then kind of feeds into this

concept that you hear a lot about in business and other

of people are finding that 80, 20, what

is 80% of the returns or

20% of the effort? And then what we find is that it

flips for that remaining 20%

of returns is now going to be 80% of

the effort. And that 20% is what I've defined really as

the last mile. And I think the

conversations I'm having with people is that they now have the tools and they've had

POCs and they're seeing results and they're seeing

even a lot of times good results. They just don't know how do I get

it to the point where it's actually returning investment, whereas

roi. And this is a question that was happening at both conferences,

both from an executive lens point and from the, you know, the

general day to day developers. And this is where I think open source

is set for in a great position because

there's so many open source tools out there that we

can work with people on, you know, finalizing that last

mile. I think what people are most annoyed about though is

that there's not a magic button that's going to fix it because it's

very much based off of your requirements, your

skills, your knowledge, your processes

that now need to be defined within

your AI stack. And that really is the last mile.

And I think that's even I saw that from Both conferences

where the realization that there's still a lot of work that needs to be

done to get AI to a point where it's actually

very fine tuned, very functional, very

efficient right now. It may work, but it may not be very

efficient for scaling, it may not be efficient for cost, it may not

be efficient for the new token economy that we're seeing.

And the last mile is historically the biggest problem to crack. Right.

And once you solve that problem, Amazon as a

physical last mile. Right. In terms of how they actually execute on

delivery. Right. Because you can have warehouses, but

everybody lives in a different house. Right. So there's a lot of little

last miles. It's death by a thousand paper cuts, if

you will. Absolutely. And we saw the same thing with microservices

of organizations that developed microservices but

then had a lot of challenges and had to overcome a lot of

that last mile when it came to data domain.

And you know, where, where does your data exist within this

microservice architecture? How do you do contracts and

handshakes between services? How do you orchestrate these services? How do you scale

them? You know, in many ways this is the

problems that we saw kubernetes kind of develop out of.

And now we're seeing being embraced now by a lot

of the same challenges we're seeing with agentic systems and

AI and how do we scale that out efficiently?

So I love what you said. It's not a new problem. It's

just the same problem we've seen reiterating over

50 plus years of compute history that now

just has a different lens to it of the

AI problem now. But a lot of the same solutions are still the

solutions that we had for many of those advancements in technology

that we saw, you know, over the last few decades. That is

interesting because like, you know, you know, Kubernetes does

has solved a lot of the same problems and it doesn't solve them all. But

there's a significant overlap and I, and I got that sense from the conference

that people are finally starting to get it. Like, why OpenShift AI?

Well, because OpenShift solves a lot of these problems. You just

put AI on top of those solved problems and it

doesn't fix everything. There's still going to be a lot more room

for improvement in terms of how you implement that

on your last mile. But it gets you

halfway there from a get go easily. Yes,

absolutely. A lot of the questions that

IBM think were it was Actually funny,

a lot of them are about IBM Bob and I know you and I have

been kind of talking about this for like last two weeks, but

at the IBM think IBM Bob was a very serious conversation of

executives wanting to know how can they

mimic tools like Claude code. Right. But

within their enterprise setting. And the biggest thing about IBM

Bob, that I learned actually at IBM think from both the engineers there and

those who are interested in it, is that a big thing here is what they

want for institutional knowledge. They want to keep a record

of all that institutional knowledge from the prompts

and the context and all the things that you

know, are built out of IBM Bob,

so that they can keep that information as institutional

knowledge. Really being able to then take that knowledge and

then kind of re injected into their broader agentic

engineering. And I think that's actually the, you know, I don't think

IBM Bob is actually really meant to be a clone of cloud code. I think

it's really meant to be a manager of institutional knowledge across

many different. Yeah, so we have a

special guest, a second special guest show up. This is

Crystal, my little dachshund pup. And I had a pick her up from

chewing wires, but I was listening. But

you're right though, there is definitely a

Bob feels different. I don't know how to describe it.

I had issues getting authenticated into it but the folks at

the Bob booth, at the my IBM booth did help with that. But

it's unfortunately named honestly I think because I think

of Microsoft Bob and that was not

exactly a winning product. Right. But,

but I, I've been playing around with it and I, you know, I had to

do kind of the init process on, on, on a couple

projects and was interesting because it suggested how to take those

projects and turn them into MCP servers and agents,

which the other ones Codex and Claude

have not. I thought that was interesting and I didn't prompt it to do that.

It just basically said on its own like, you know, you could turn

this process into an agent MCP server and things like that.

While that was in the back of my mind as I, you know, built these

various projects, it was not top of mind. So I thought that was interesting.

Yeah, it definitely is not a clone. It's. It's meant to solve a new

problem. Yes,

I agree, I agree. And I think it really

starts feeding into this bigger scope of things like

sports perspective and development. Right. And these other tools

of like how do we get the knowledge out of the project managers, how do

we get it out of the JIRA and how do we get it

into a way that the AI can

interpret it but not lose that knowledge along the way?

So as the prompts are coming in, as the context are coming in, it then

comes part of that institutional knowledge. And I think that is

ultimately what Bob is trying to achieve. That is very

different than I think, what a lot of the other alternatives out there are.

My hope is that as this grows, we see more opportunities for

it to become more open source. That's probably one area

where it's a little different than what we do here at Red Hat, where I

think we, I mean, we're not supporting the project,

but a project that we're very closely buying is things like

open code, for example, which is an open source alternative

to cloud code. It's really interesting to see all these

different solutions right now. I also like the fact that Bob can be

an IDE and mimic more cursor, or it

can be a CLI and mimic more of a cloud code, which obviously

with my background I'm more comfortable with the CLI side

now. That was a big one. And I would say then obviously agents would

be the second biggest thing. Just in general, that was the theme last

year at IBM Think. And that didn't change this year. I

think we're just seeing the experimentation of

agents now, moving into the

solidification of agents in the industry.

And I think we heard about agents a little bit at a high level

at IBM Think. But then for Summit, everything was about

agents. Everything went down to

how does this implement to the agent, how does the inference of AI

implement to the agent, how does the data implement

to the agent? You know, the orchestration layer,

kubernetes, all these things. It all had to do with the agent.

And that was really interesting to see how the conversation

over the last two years has shifted from all

of these individual parts. I think the last time I was on, on your show

and I know you and I have talked a lot about, about how AI. There's

been a lot of these parts, but nothing has kind of unified them.

I think what we're seeing with AI agents is going

to be that unification. The agent will become the unification part of all these

different parts of the AI industry where all these tools now will come

together. And we saw a lot of that at Red

Hat Summit. You don't think that'll be. Harnesses will ultimately

be the container for that, where all these things will live and harnesses will be

kind of like top level abstraction. This is a really good

question because this is the big debate within the AI labs

and the AI community, are you invested in

harness engineering or do you think the models

themselves will just supersede

the harness and that they can be knowledgeable

enough to basically function agentically without.

So obviously the open AIs and the

clouds of the world and anthropic. They're probably a little bit

more on the model side because that would ultimately benefit them.

Right where I think the IBMs and the

Nvidias and I would say the majority of the industry

is probably a little bit more on the harness side because that allows

a larger ecosystem of third party tools and something

that's a little bit more familiar to people. I don't know.

I think over the next year or two it'll definitely be harnessed because that's where

we've seen the most advancement. But with things like mixture of

expert models just continuing to advance and how

they can do reasoning and they can do a lot of agentic.

It could be that we see the model layer

chip away at the harness layer and is this going to be a back and

forth and it really just gets also into

how do you inject the context. And this is closely

related to the same argument of is RAG still needed?

With context size growing so much, why would you need rag? And

I think from an enterprise standpoint, and I think Red Hat is

very big on the harness side because we see the

need for different security layers, different different integrations into third party

tools, different

authorization layers, routing,

networking that the model

will not be able to manage completely, at least for

a while. And that's where I think the harness engineering layer will

exist because there are all these existing technologies

that the agent needs to integrate with and that's all going to

happen at that harness layer and then be

executed within that runtime layer. Yeah, that's how I see it

too. I think the harness layer is really going to be.

It may not be a foundational type situation where you build on

top of it. I see it more as the mortar between the bricks.

I agree. Right. Like, and it's not

that the mortar is more important than the bricks, but

the bricks are kind of pile of rubble

unless you have mortar kind of holding in place. That's kind of how I see

the harness story evolving.

But I have a hard, I, I have a hard time

imagining models ever being able to be that far advanced.

However, you know, we've gotten

further with the LLM architecture than I ever thought we would.

Synthetic data has been more. And

distillation has worked better than I ever thought it would. So Take.

Take my thoughts with that in mind. Right. You know,

when. When I looked at synthetic data and kind of

distillation in particular. Right. There's a meme where they show,

you know, somebody fishing in the. In the water, and then somebody is

fishing from that guy's pot, and then from. Somebody was fishing from that

guy's pail. Right. And then they show each subsequent

fisherman was like, more and more distorted. We've not really seen

that come about. Right. It's not like you're copying VHS

tapes where subsequent generation gets

worse. I'm sure that if you don't do it carefully, you'll

get some weird artifacts. But it's not been.

That has not been a default case, which I think is interesting. It

is interesting too, because most of the models that are out

right now are

distillations of actually GPT4 family. Right.

Even the GPT5 is still a direct

distillation of 4. It was not completely retrained.

And Anthropic obviously has their first generations and second

generations, but we actually haven't seen very much

new generation just because how expensive it is to create

from. From fresh. And from. What I'm imagining is that they've

tried and it's just they haven't gotten the results that they wanted.

So I think that will be what we see. I don't know. I haven't heard

if Mythos is a. So if people aren't following the. With Mythos

model from Anthropic, it's a.

It's a model that they've withheld because supposedly it's too

risky. I don't know if that model is.

Is a whole new generation. I would imagine that it probably

is. But to your point, most of the models are out there now,

and what we know from the Chinese models, that they're all just distillations of

the American models. We have proof now that they've been

mass API, hitting the

GPT and Anthropic and Gemini to

create the generation of Chinese models that we have now.

So that's something. And they get. They're very performant. Like, those models are very, extremely

good. Very good. I mean, it just shows you like this. This is

not the paradigm of, you know, analog VHS copying. Right. This

is more. More, I guess, in the style of, you know,

remixing an old song digitally. Right. You

don't really get it's. It's not

a well thought out analogy, Christopher, but. But, you know, you'll hear

them on I don't go to Clubs Anymore, but on my, on my what's

New and what's Hot techno playlists on Spotify.

Right. I, I recognize the same backbeat, I recognize the same

chorus, right. Like from songs from like 20, 30 years ago, right. Like,

and even sampling and rap music, right. Like, it's a bit more like that

where you do get a completely fresh perspective based on older parts.

And that's something that I did not expect. I, I just assumed that it would

be some kind of. You would start getting really bizarre artifacts after

so many generations. But that's not been the case. So,

you know, I think it's interesting because we really don't. This is really uncharted

territory, right? These are. Yes, they're based on very well known mathematical

principles. But like, as these systems get more complex,

it's getting harder and harder to predict not just their behavior, but

the range of their behaviors. Yep. One second. I'm going to grab

something because we'll do a little bit of show and tell as well. Cool, cool,

cool. So while you're away, I will

maybe I can interview a dachshund. So what is, what do dogs think about AI?

Everybody and their cousin and their dog has a AI startup now. So what's your

AI startup? Oh, a link shortener.

Okay, cool. Because I get it. Your short

legs. I get it. That's cool.

While we're waiting for Christopher to come back, you all know I'm a big

fan of Humble Bundle, so. Humble

Bundle. Oh, you're back. Cool. Oh, you can finish your thought.

Humble oh, so Humble Bundle. I actually, so I

worked the booth. I had a, A talk on day one and I worked

the booth on the subsequent days and

you know, a lot of people came by and

other Red Hatters. Actually, I, I was showing them

Humble Bundle. I'm sorry, go ahead.

No, I just said that looks really cool. Yeah, yeah. So if

you're not Familiar with it, humblebundle.com it started as games,

but if you go and you pick store.

Not store, I'm sorry, bundle,

you can pick books and there's comic books there. But there's

also a lot of stuff here that is particular around

software. Right. So in this example here,

this is the books on practice

exams for AWS and gen AI,

all sorts of interesting stuff here. Security.

This is actually a hybrid of like courseware.

So they also have software oops.

Bundles that are, you know, sometimes it's kind of like image editing

tools and things like that. But very often

they Will have courses for, you know, how to get into Open Claw and things

like that. And I know if you don't know, Christopher is really into openclaw,

he helped me get my Claudia kind of up and working.

But if you go here, I know a lot of listeners Data Driven are

big into Power bi. These are basically courses on Power Bi

and things like that. And the cool thing is it's

$20 for 17 courses and a

portion of your cost goes to a charity.

So it's really cool. You get a lot of material and you know, a

charity gets funded and things like that. So definitely

check it out. They often have AI books or,

you know, app development books. A lot of things around

game development too because that's kind of where Humble Bundles started.

Nice. That's a great segue too because speaking of

openclaw, so when I got home from

Red Hat Summit, this arrived. Oh, nice.

So I haven't gotten a Mac Mini in

probably like over 10 plus years. So when this came I was

kind of like. Because I don't know if you remember, the Mac Mini

was maybe about the same size, but it was much bigger than this.

And I'm actually holding this with like one hand right now.

But the reason why I got this is because

Red Hat in particular wants to make sure that all of

the agents that I'm running for Red Hat are isolated

for runtime. So I could use

my. Let me see if I can pull it over. You have one of those

framework things, right? This is the framework. Yeah. This is the

size of it. So that is actually

powering my home Lab that has OpenShift in it. I could

do that. And that's actually where a lot of our tooling are going

towards. But I also need a

agent to have access to my email, have access to like more

like my day to day tooling which actually exists more. More on a

desktop. And that's where this guy comes into play.

It's interesting. Now we're separating

the harness from its runtime and now I'm dealing with

multiple runtimes. I'm going to have runtimes that probably run on the home lab

and now we have runtimes that are going to run on this.

This one would be. I need it to do something that actually

involves some kind of GUI or something that's already on my

desktop, which just there's no API set up for me to do.

Or I need it to do something, maybe some, something basic that's really easy to

do within the Mac ecosystem where this

like my home Lab, maybe it's an agent that's running diagnostics

on like AI ops diagnostics on my

home lab. Why isn't something up? Why isn't it working correctly?

And this is where the whole concept of

runtime now has become such a big thing. And I think it will continue to

become more important this year. Harness is kind of getting the

spotlight, but we need to move more into this runtime conversation of

okay, now the harness has put the context together, it's put all the knowledge together

and the skills. The agent is running the agentic loop with

the model. But now where does the output actually run?

Does it run on your, your personal computer

where it has access to sensitive information and you know, it

could do things that it shouldn't or does it run in an

isolated environment? So this is probably going to act more as like a little server

that runs here in my office where

agents, this is just for agents. This box. Where is the inference run?

Where does the inference run for your agents? Is it run? Yeah.

Does it run on that? Does it run on your framework or does it run

in a hypercloud service? So I'm actually

doing a new technique called semantic routing.

All my requests go to my home lab first. Within what we would call the

control plane for the agent, there's a router that

exists that actually evaluates the information that's coming in and

decides based off of sensitivity and complexity where

this route should go. About 80% of my traffic actually hits

the framework for a model that's running within Vllm

on the framework device itself on

OpenShift and then about 20% where I've deemed kind of high

reasoning. Then we'll get sent off

to our corporate Gemini account that we have within Red

Hat. So this way it's also really

nice because when I first started working with agents all the way back, I mean

I've been working with agents for, for years and years. But our current

modern day idea of what agents look like, back at

the beginning of this year I was running out of

tokens. I was getting throttled by, by Google and there was

nothing I could do about that because that was part of our corporate account. It

wasn't anything to do that I could go and change the knobs. So moving to

this semantic routing approach allowed me to not

run into that throttling anymore. Most of my things go. So right now I'm running

a quinn, the quinn 3.6

35B mixture of experts model. Nice.

And that's running right now and doing all of my local agentic work.

It's doing most of the low reasoning tasks and then all the high

reasoning tasks then get sent off to Gemini.

So do you ever have it set up where the high reasoning task will divvy

up a bunch of low reasoning tasks and then send that down to your Quinn?

Or is that something in the works? I have

experimented some with that. So

that gets into some like post inference type of techniques that

we've been experimenting with, myself included.

I haven't gotten that far yet. This is where

areas such as like speculative decoding kind of come into play or

post inference technique. Why would speculative

decoding come into play here?

Yeah, because there could be a speculator that sits

at the local model that actually

acts as kind of almost like a guardrail to the

larger model where it can actually start reasoning about

some of the things earlier on and decide

basically acts as a breaker. I got you. And that makes sense.

That's where speculative decoding would be kind of the

next iteration on that where

it's really the management of knowledge and memory and cache at that

point. I really haven't gotten into that with my local setup, but that's

part of that whole last mile where memory I

think will be the last portion of the last mile for

everybody. It's going to be memory management, it's going to be cache management.

When you say memory, organizational memory, not necessarily the physical memory.

When I'm talking about memory, I'm talking about the memory of

the agent itself. For

OpenClaw, for example, every time it makes decisions,

it keeps a compressed record of what it's done in these

JSON files and then it will reference that

your cloud code does something very similar. Every time you hit your token

context window maximum, you'll see that it's doing a bunch of

compressions and it takes a little thought. That's actually what

we call a form of memory. If you've actually been following the news.

Even just today,

Google IE just announced a whole new

agentic memory platform, a

framework that fits right into this. And that's why I think memory

is going to be the next iteration on. On,

you know, improving the agentic system. And that's not the KV cache,

that's not your physical memory. It's not the

agentic memory would be a. Yeah, it's like a gentic memory. It's how your

agent has recogn

reconciling what it's doing doing and has. It's. It's

outside of the context window, but it's not the KV

cache. It's something that's like, oh, this is what I've done in the

past and this is the context I need that I just

need to keep carrying forward in my conversations.

It's something that maybe it's not an MD

file, it's not like permanent knowledge. It could get flushed. You could just say

go ahead and flush your, your memory

and that may actually be what you need to do because maybe it's. There's a

lot of nonsense in there or something that's doing something wrong. It's not

meant to be long term. Think of it like human short term memory. Exactly what

it is. Interesting. Not everything that we do is long term.

So long term memory in this case would be the, your

MD files, it would be your kv,

potentially even like some layers of your KV cache where I

would actually consider that more like intermediate. But it's really that

long lasting context that just keeps getting injected in where

this concept of, of memory that we keep hearing about

is more of that short term memory of what knowledge

do you need to have right now to make the decisions that you need to

make based off of the reasoning and the topics that you're working with

right now? So a good example would be. I'm sorry, go ahead. No, no,

no, no, go ahead. No, good, good example. You like your hotel room number when

you go to a conference, right. Like you're never going to cancel, you're having, you

know, needing to remember that beyond once you check out is very low.

Or when you get the two factor authentication, the six digit code. Right. You only

need to remember that for a very short window of time.

Yes, exactly. And that's a prime example where

you could long term forget that information. But in the

short term it would be very detrimental if you forget your hotel room, you have

to go and ask somebody and that, that takes time. Yeah, it takes time and

that's exactly the same narrative. It's not that the agent couldn't get that information,

it's just that it's faster for the agent to get that information if it's

located in some type of short term memory. And that's

where we're seeing so much advancement in, in these

agentic platforms. Did you want to, did you want to add

anything to that? I know we're coming up to time, so I just. Oh no,

I mean, no, I appreciate your time. I see the, I see that we' up

on time and you know the.

No, I think there's a lot. I think, I think the one thing I learned

this week was it's very easy to think that you're behind

everyone else. But you know, we've had people, we had people come in the booth,

like, I don't know anything about this, tell me where to get started. And I

both shocking and, and

refreshing. Right. You know, there were, there were people. I'm not going to name

any names, but like, you know, there are people who are in our

division and they've not even installed

Claude yet. Open Claw. I mean, I

always get those two confused, even though I know they're very different things.

But, you know, who've not installed Open Claw, like

on their own? And it's just like I feel behind because I

have Open Claw, but I don't have it as set up. Well, set up as

you. Right. But I do have it, you know, so it's kind of

like it's, it's, it's, you know, don't be afraid of being behind because

chances are you're probably not. No, no. Part of the reason why

I have the dog do that intro now, which of course is, you know, obviously

AI generated was part of the joke of that was everybody on their dog is

an AI expert now. And there's not

really any experts. There's probably about half a dozen people

worldwide that really are on a whole other level.

I mean, the Andrew Angs. The Andrew Angs of the world. The

Jeffrey Hinton's of the world. Right. Like those are the people.

Yan Lecun for sure. You don't

hear much from Yahshua Bengio anymore. But you know,

like people at that level, right. At that, that strata, like

they are, they really are like that far ahead.

And it's always interesting seeing like what problems they're trying to solve.

I think is very interesting. What is particularly interesting, I think it was. John Lecun

is very skeptical of LLMs getting any further

along. Yeah. Which I think is

interesting. I mean, it's, you know, at this point almost a 8 or 9 year

old concept of LLM

transformers. The concept that he created. The concept that he created. Right.

So the underlining layers. Yeah, yeah. So like a lot

of. Go ahead. I was gonna say there's a lot of new

interviews that he has out in the last couple weeks about, you know, his

new approach to AI and how he

sees it superseding LLMs. And that'll be interesting

too because he's looking at it from a whole new direction than just

how LLMs just, they're just,

they're just building the next pixel the

next text where he's looking at it from a whole new

direction of, you know, maybe we built this

house of cards wrong. We need to just kind of start over and basically

like, stop, start at the basics and, and build something better from what we've

learned. And it'll be very interesting to see what he comes up with out of

all this. Yeah, I, I, because I, I'm surprised we've gotten

this far this fast with LLMs. I, I

really thought, like, the whole reasoning aspect to LLMs is something I

did not see that they were, I did not, I would not have bet real

money on them being able to do that. Right. But here we are. Like, they

clearly can do some level of reasoning. How much is probably

debatable, but the fact that they're just, you

know, you hear that they're just like text prediction thing algorithms on your

phone where they predict the next word. Well, technically true,

I think doesn't really tell the whole story. Right. Like, that's like saying that the,

the F35 fighter is the same thing as a

paper airplane. Right. Like, they do have to apply, they do have to obey the

same laws of physics, thrust, lift, gravity, blah, blah, blah.

But they are very different animals in that sense.

Very much. I agree with that analogy. It's really good. Cool, man. I love to

have you on the show again. We could talk open claw. Yeah. You've done some

crazy cool stuff with that. Definitely. I know some of the

agents that you've built that people probably don't want me talking about because I know

you made a lot of it. Security people very nervous.

That's true. But the stuff that you've been able to

automate has been nothing short of like, oh, my God, that's amazing.

And also super useful. Crazy too, for me is that I've

been so busy that so much of the stuff that I did that people were

talking about was like one to two months ago

and I think this summer. So there was actually a,

a really popular podcast out of the the

AI Daily Brief that went out where he was talking about how

everything that's happened over the last six months basically came out of

Christmas break. So, like, everyone went home and

had a few weeks to just like, play around with this stuff. I was one

of those people. So, like, so much of what I did came out of those,

like, experimentation phases. And I think I have to repeat

that this summer because there's so much new things that we've learned.

Right. That, But I still haven't built on top of that yet. And I think

so for me right now, so many of my agents are doing very simple tasks.

They're doing information gathering, they might be looking at

meetings and suggesting that I read certain articles

correlating to something I'm about to talk about. But I want to go the

next level where I get into like a multi agent system where

I have like a chief of staff agent who's got one

that's doing programming demos and then I have another one that's doing

like general administrative assistant work or another

one that's front facing, you know, I

a model that's on our slack that people can just ask questions to

based off of my institutional knowledge that I have of, of

our company and our industry. So that's the next phase and that's

where the memory stuff has to come into play and the multi agent kind of

orchestration and all these things are things that are being worked on now. So there's

not like a clear winner or a clear understanding of what's what

that looks like right now. But we're all kind of playing around with it. So

I think that's kind of the next phase. And yeah, I look forward to coming

back. And I think that's probably part two of this conversation will be absolutely.

What does that look like? What are these tools? How do we kind of build

on top of this thing called openclaw or

Hermes or all these other ones that are out these days. Yeah, that'd be

awesome. And even if we just do a deep dive on like kind of what's

exactly, you know, what's what. Because I know you mentioned a couple of things that

maybe most of our listeners don't fully grok, right. Because

we have a lot of data engineers here too. Right. So and the

other thing too that really came out was people would ask me questions about because

we have something that Microsoft folks may know as TFAs or technical focus

areas, call them pillars. So you're the agentic lead, I

believe, and I'm the connecting models to data. Right.

So the rag and that sort of thing and you know, a lot of the

conversations I had was, you know, data engineering is

more important now in AI systems than they were in the past

because I don't know exactly how

rag agentic systems would fail but,

but when they fail, they probably fail very spectacularly.

But I know with, with rag systems, right, you know, if

your data chunking strategy and your data kind of

indexing strategy is not, I wouldn't say perfect because you'll

never really get there, but appropriate to the data source documents that you're dealing with,

you're not gonna. It's gonna fail in a way that is subtle and is only

gonna amplify get worse down the road. Right. So you

really have to think through a lot of these things. Right. The, the one sentence

I said most of all was, you know, chunking

is an architectural decision. Yes. It's

an important one. Treat it with that importance as opposed to just whatever,

you know, paragraph by paragraph or blah, blah, blah, blah, blah.

So that was other consistent theme. But I will say that the, the questions that

I get are far more evolved than I haven't gotten at any

other conferences in a while. I agree.

Especially this year. It's just a step up from where we were. So. Yeah. Cool.

This is great. Thank you for having me on. No problem. We'd love to

have you back. And since the recordings for the podcast, we'll let the music play.

It.