Hello, this is Eva and you're listening to the In Between Tech
and Trust podcast. And in this week's episode, I
get quite exciting because we talk about one of the most
pressing topics in the tech and the ice era.
And these are AI agents. And if I had to give it a title,
then it's like the one O 1 of agentic systems for
organizations. And for that, I have Anthony
Alcatraz, author of the O'Reilly book on agentic graph rag, a
senior figure of AWS pushing agentic AI adoption at
enterprise scale. And he's also a business Angel
working with early stage startups that are actually
building, scaling and incorporating layers of the AI
economy. And so with his background and
his daily operations, Anthony sits Anon quite unusual in
between an intersection because he did deep enough in the
technical architecture to write a book and to co-author it, but
he's also close enough to the business reality to know where
organizations, corporations and teams currently are struggling.
And so in our conversation, we get like so knee deep into what
actually changes when it becomes more of a systems perspective
and when it also starts making decisions and taking actions on
your behalf when agents take over and do the research and
feed it back into an organizational output.
And so the tension that we looked into is that a genetic
systems, if they are already, you know, incorporated into the
organizational structures in a way that users cannot see them.
The biggest question that we need to discuss is how we build
trust into something that is invisible and that is yet to be
defined and to be educated. Welcome Anthony to the In
Between Tech and Trust podcast. Thank you for the invite.
I want to start with you at the intersection of technology and
trust, and particularly when you think about it and from a
context of the Gentek AI, but also from your collaboration
with start-ups, what's the first thing that comes to your mind?
In term of the technology and trust that is to say especially
for for I think currently it's a very clear change, OK.
Before we had system that were mostly reactive, I would say.
So you, you would ask a question, you would get an
answer. Now, which change considerably
with agent CKI, we can't define agent CKI, but but system
basically that's observe, reason and act.
So when will you have such system?
I would say the trust, you need much more trust than before
because basically you, you are delegating what we used to do as
I mean, people human to a system that is automated that will make
that, that which reason for us and that will take decision.
So if you think about implementing that in regulated
industries like, I don't know, healthcare or even legal, legal
system or insurance and that it says you, you need to, to have
a, a clear trust in what's the system is deciding.
And I saw to put, I would say the human expertise that is
still needed at the right place. So it changed considerably.
I would say the data governance I wanted and even the governance
I wanted. I think there is a problem
currently because we don't have, I would say currently there is
no clear framework or governance that has emerged to manage at
large scale such system. I mean currently we can
generate, we can like build, build the system, scale it.
This is what we are doing at Entropic, open air etcetera.
But I would say the governance infrastructure of it is not
quite there. We, we, we struggle for example,
to secure the security risk etcetera, so.
Particularly when it comes to building a genetic systems, you
work with startups, but you also help them scale into like
environments that then also enable them to grow and to
transition into this enterprise context.
What fundamentally changes when AI shifts from a single use case
and maybe also in an early stage company into a system setup that
then also need to be robust, need to be trusted and need to
be able to also be, you know, as flexible as the market evolves,
but as stable as the company or organization requires?
Yes, yes, yes. So, so it's complicated, OK,
frankly to to put into production agents.
So you need to understand that before we, we had system that
were stateless, OK. So basically between
interaction, it didn't have to remember, didn't have to
forcibly connect with internal data, etcetera.
Agent 6 needs to be stateful to work.
So you need to build a a system that's basically maintain
context. So to maintain context, you need
to access a context, you need to store it, you need to maybe
classify it to understand it. On top of that, most of this
system are building up as some memory capabilities.
So you need a memory system, you need to manage this memory.
OK, currently I will cite an example from Munich Cogni, for
example, this it's a graph databases building memory system
for for agents. And this agent need also to
learn from VZ. So basically you need to build
an evaluation situation system. You need to make sure that the
agent that you are putting into production are are performing
well. And to build such evaluation
system is still is still difficult.
I will set you one problem with the complexity of evaluating
agent is that agent are multi step.
OK? It's not it's not only I give
you an answer. Is it right or wrong?
You have multiple dimension in this.
You need to to to measure as it come with the right step.
Are these steps secure? Are these steps efficient?
Did it consider enough source when he when he looked into your
data, internal databases, etcetera.
So it's it's much more complex and This is why I think so.
This is my thesis and this is what I'm writing about with my
book in my O'Reilly agency graph rag.
I think graph architecture are pretty useful to manage this
complexity. But before we like hint there
and also dive a bit into the context of it, I want to linger
with the agentic systems because you've now explored on how the
impact is in applying it. But when we come back to an
architectural point of view, what is the big shift for
organizations that they need to do, that they need to apply and
that they need to evolve into when they wanna translate their
structures into energetic flow or Yeah.
This is big OK, because basically what is happening is
that everything that can be accessed, OK is automobile.
You can automate everything that can be accessed.
So enterprises need to think about this accessibility of our
current databases, tools, etcetera, because everything
that can be accessed, consumed by agent is like food for it.
So basically you need to have like a clear strategy about what
does my agenting system need to access.
So you need to build like some sort of gateway.
For example, I know publicly companies like like big banks,
Morgan Stanley built a huge gateway of all of their tool,
all of their software are are accessible by their agents.
There is a a public like an MCP gateway, OK.
And there is this concept of course of MCP.
I think we don't need to go in into detail technically, but
basically MCP it's like a USB key for for agent.
That is to say it's a protocol that allow agents to access a
certain tools or a certain piece of software.
So you can transform your existing software like
Salesforce, for example, MCP, Salesforce allow an agent to
access basically Salesforce. So the current opportunities,
the, the customer notes, etcetera, extra.
So if you think about it at large scale, you need to map out
what are your current capabilities, what's your, your,
your, your, your user, your, your, your, your employee
accessing their work. To rationalize this into I would
say a main gateway. And when you think about this,
there is complexity because you need to manage your identity.
Maybe when I work in a share, I got access to tools related to a
share, but I don't want my sales agent to access this, a share
tool. So you need to build this
identity layer that is to say preventing agent to accessing
tools that are not authorized for them.
You have many complexity around it.
And for example, so I don't speak as an AWS employee, but
AWS for example, has deployed agent core OK, that help builder
build agentic system. And here in agent core, we have
agent core identity that's manage this credential identity
layer for for agents. So, yeah, to sum up is access is
agentic and it creates problem because you have many system in
enterprises currently that are not accessible by IPI or MCP.
So you have this integration layer that get that can get
complexified by by this old system.
And you will need to have and I think it's already the case and
the engineering of this old system to be accessible by
agents if you want to identify such processes.
And interestingly, you now talk about this unifying or
interoperability of those layers of integrating it with a tech
infrastructure that combines the scattered use case that you
currently find in heritage organizations, right?
Yeah, I assume that this is a challenge, but what are the
three biggest challenges that you currently see for
organizations when they want to deploy a genetic systems or also
transition from experimenting with AI to fully operationalized
systematic collaboration? Yeah, yeah, yeah.
So first one that come into mind and it's it's always the same
for AI. OK, Well, whatever AI you you
consider like machine learning or or no modern LLM surgency
system, it is that architecture of fragmentation and it's data
quality basically. One problem is that the system
of record that we built for example, it doesn't contain and
it was a recent thesis from Foundation Capital in the US
around context graph. What we are saying is that in
the current system of record, you don't have the why of the
decision you have you have the what happened, but you don't
know all such decision out such data was created.
If you want to have autonomy agents that make decision, we
need to access the why, the criteria, the factors that's
that's that's allow such data to be produced etcetera.
So this is the first problem. Second problem is that we
studies demonstrate that agents like particularly for example
graph architecture, because graph architecture allow to
connect. So once you have an anthology,
you you archive rationalize your processes to to the facts.
To have it within a graph architecture diminish diminish
complexity to access this data. It's reduced for example the SQL
query that you would make if you were only to use tabular a beta.
If you take the example of vector search only, the problem
with vector search is that not everything can be retrieved with
only semantic similarity. So basically the first problem
is that you need to think of that architecture to be agency
friendly. And this would be my my first
problem. The second problem is basically
the governance that I mentioned earlier here, here there is
many, many thing I, I, I would demonstrate the the governance
problem with the security risk recently, so there has been an
explosion of this thing called clobot.
We know for a fact that there are risk reversions.
For example, there is this risk called tool poisoning.
Tool poisoning is is that if see an agent access a tool that is
online OK and that this tool contain for example malicious
instruction that are hidden to human OK.
This could be like instruction that can't be read by human.
You could basically contaminate the agent, maybe modify his main
behavior and have like a Trojan horses, that is to say an agent
that's send your data or modify your system without you notice
it or access databases etcetera. So basically agents are so
powerful is that if you contaminate it, if you low
external threat to act it, the consequences can be really big
in terms of data exfiltration, data modification, ETC.
So all of these governance gap that is to say is still
emerging. So you you have a lot of startup
in Israel, for example, the Israel Israelian are the best
around this security risk and in governance I did not mention,
but you have the evaluation so many of our of our problem.
And the third because you ask for free is basically So coming
up to your, your first point is a trust that is to say that is
to say you need, I think to you need to demonstrate because the
problem is that this system of course for sure will impact
current occupation. I estimate myself that maybe
1995% of knowledge worker job will be impacted.
What will stay I think is I impact customer meetings this
kind of stuff, but to say what is intrinsically human for the
rest all of this, all of that is administrative task that
analysis etcetera. All, all of the knowledge deep,
I mean the routine knowledge work or even the deep one, OK
can either be supported or automated by agentic system.
So you will need, I saw in term of change management, I think
rework around the current processes, what people are
doing, what are their roles and will amplify.
So I am not pessimistic about this I think.
I think this system allowed to intensify the work.
It's allow everyone to do much more, not less.
But interestingly, I want to just briefly iterate on the
trust topic that you touched upon because connected to your
second challenge that you pointed out.
Yeah, when it comes to the design of them, you explain to
us that they can also be designed for misbehavior.
So how would we, as appliers of an agent that has not been
deployed or built by ourselves, how can we trust them that they
act with our best intention in mind?
Is there anything that you could advise us to either look for or
make sure that once we apply it that we are prepared for the
next or for the output? Yes.
So basically there is like basic, I would say security
tools that can be used to prevent, I would say most of the
risks. For example, if you take the
identity one, the fact that your agent is only allowed to access
your own tools or exercise, it's it's reduce the, I would say the
blast radius of of adacking basically.
But you have like tools like for example sandbanks sandboxing,
because before your agent is maybe doing a query or doing a
coding activity, what you can do is to 1st sandbox it, observe
what he's doing and honest is validated by an external system
can do the actual coding. So there are many, I would say
technologically, I think there will be more and more solution
to prevent either the plus reduce of a of a problem or or
even completely prevented. OK, I will set you an example
that is this company called the TST, They work.
They they work currently. So they are was a creator of of
a library called outlines. It's it's structure output for
LLMS basically. So it's very simple for everyone
because I know our listener won't be forcibly technical.
But what it does is that it forces the LLM to respect
certain rules, basically when it's watching, when it produce
prediction and and output. So this in itself, when it is
placed at sensible points of the agentic system, prevents most of
the risk because you make sure that the agent is respecting
your schema, your rules, your procedure.
And this company, by the way, is working on, on something very
interesting. They are working basically on
the control layer for agent. So basically with your question,
I think trust will come from the emergence of this, this, this
building up of this control layer for agent that I know many
startup are are working, working on.
But for enterprises, I mean, of course you need red teaming.
So you need a team. So red teaming is 1/2 people
internally that are testing for bad behavior.
Basically you need to test for the bad usage of the agent.
So to work against it basically, of course should you need to
have that in mind, you start up are are also proposing that to
to enterprises. But I'm sure many enterprises
that have that have a strong data science team or data
engineering team or data engineer team are doing doing
that by by themselves. Yeah.
So, yeah. Sorry, because you just explain
to us the difference between the startups and also the enterprise
setup. And I would be interested to
dive a bit into the business Angel aspect of yourself and
also how you collaborate with startups at early stage.
Yes. If we look at them from an early
stage perspective, what would be a differentiator for startups
that understand how and trust architecture is being set up
versus those who chase like the hype or also the progress of AI
and itself? I mean, the best doctor that I
met basically first, they they understand very, very precisely
all the current processes are are are working within
enterprises that are they're trying to improve or or
disruptive. OK.
So they they passed most of their first months or time to
meet to meet basically the business owner of these
processes. They speak to them.
They have a clear understanding of all all it is going on
currently within the auto prices.
Most of the time one of the Co founder is coming also from
these processes. So he has he has already a
network within the auto prices to support his first go to
market movement, this kind of stuff.
So this is very important because you need to clearly
understand, but what is your it's regulation landscape, all
the processes are what will be the point of integration I saw
about as mentioned earlier, maybe there will be such
challenges to integrate with the system currently put in place.
So you need to all of this inter standing at this background to
make sure that after all you can have a clear go to market to
disrupt or improve the current enterprises.
So I, I have example recently, I mean, I must see this kind of
start up every week. And when I evaluate them, I have
this this clearly in mind. It's a social angle, I would
say. Does this start up as design
partner or as access to the enterprises they are trying to
improve all the processes they are trying to improve.
You can't work in isolation. Basically, what will be valuable
is access to I I have this example of entropics Entropic
recently did Nakatan and what is funny about the Sakaton is that
the third so the three guys that were wanted were not developers,
they were people. I think it was like a doctor.
So they were basically the the business expert and and this
tells something like basically I think the most of the creation
as a value will come from business experts that were
backward from virtual and processes virtual and problem
that have no the mean with agent decoding to code it.
We'll cut it and we'll come up with a start up ID and try to
describe the current way things are done.
So basically. Yeah.
And now I guess when we look and also come back to your book that
you've written and you just highlighted about the entrance
of startups into, you know, markets, but also how they
approach the orchestration from the infrastructure layer and so
on. In your book like Agenda Graph
Rack, you explore also how it advanced retrieval or general
orchestration architecture is being set up.
Yeah, writing to you and also of what you've written in the book.
Why is retrieval architecture so central when you wanna build
trust in AI systems? So basically if you have like a
metaphor, LLM are just they are not databases.
OK, LLM are used when an energetic framework only to
reason open some data. So because and in most
industries your data will evolve over time, OK, if you take the
legal system every month, every week you have like a new
regulation, you have courts, courts that are that are doing
their business. So you need a way for agent to
access freshness in the data. You need a way for agents to
have an infrastructure that allow them to reason correctly
on what is happening and I found myself by building such system.
That's the best way to manage it long term.
OK is to to build it with graph technology basically why because
graph technology I lost something that is very powerful.
It's called the multi op reasoning.
That is to say you can start with a first inquiry about I
don't know. So we you have maybe so I need
to find a customer of my customer, OK.
And I want to understand the customer of the customer of my
customer. This is multi op reasoning.
OK. And you can only do that with
with rough basically. And agents are really good at
navigating and all topic has demonstrated with cloud cuts for
example, are really good at navigating this kind of multi op
reasoning and trace back basically the information.
So increase the access accessness, the assetsness of
the data. I mean, it has been measured
currently most of the industry is has been has been doing a
vector search, OK, and only vector search because basically
you can use a vector such as over graph.
OK, if you measure so you you put a both by comparison, there
is a 30% accuracy gap between only using vector search by
itself or using graph rag solution.
OK, and 30% is very big. And then so in the book what
what we explained with my core for what we explained with my
core for is that there is also this self evolution self
evolution layer. So cognitive strays on the the
memory, but memory system are very complex, OK, because when
you have memory, you need to have a system, but forget on
Casey information, OK, you need to choose what to remember.
You need to and make sure that when you remember something it
is attached to an existing memory etcetera, extra and what
most of the of the memory builder are doing, so there is
cognitive, there is memo etcetera.
They are using graph to do that. OK, It's the only way.
It is the only data structure that allowed to have such a
complex external memory to achieve this.
This basically. And in the industry we talk a
lot about reducing hallucination or so you know, being precise on
the output and very accurate on like the explainability of the
output itself. What role does graph based
reasoning play in? Yes, yes, yes.
So it's really good because yes, so you, you increase the
accuracy of the of the retrieval.
But if you think logically, even if you retrieve correctly, maybe
the LLM will interpret it wrongly or do approximation or
over interpretation, etcetera. But what is great is that
currently technically you can make sure that the LLM of the
agents will respect the ontology of the, I would say the semantic
layer of the graph that has been built.
So what I am seeing emerging, and This is why I'm writing this
book, is that the control layer for agents we come mostly and
from a concrete and organized ontology and graph and to be
sure that the agent that is interpreting it or using it is
respecting it. OK at the token level.
That's to say, currently the problem is that LLM are black
box. But if you retrieve correctly
from the graph, you no, no thanks to the possibility of the
graph. And I mean, it is symbolic.
So you know which, which nodes has been queried.
So it is part of the graph that is related to the concept X
star. So you can have a layer of
explainability thanks to this graph.
So you, you get it from SQL research.
OK, but it's less less efficient and, and, and let's, let's,
let's powerful relevant graph, but you don't get it with vector
set. So basically it's increase.
I saw the explain ability of it increase the traceability of the
reasoning chain and the explainability of the of the
graph. You know, while you're speaking
and while I appreciate if we have this hands down very deep
technical conversation right now, I'm just like, I, I'm so
humbled of all the experience. And at the same time, I'm
currently like, I'm asking myself how much of the knowledge
that we just discussed and also, you know, translate into how
it's applied and where it's also brought into the organization.
How do we ensure that people within organizations feel
empowered to understand of what we're actually talking about and
how they also can shape it? Because in my concern that I
have is that within organization says currently this friction
between people using infrastructure technology to do
their job and IT departments are like experts that are dedicated
to provided. However, with AI and the genetic
systems, we all come empowered to use do apply and develop the
infrastructure of how we want to work with it ourselves.
But a lot of employees do not yet have the knowledge of how to
do it. So they also ask, you know, the
experts in the organization to provide them with the respective
infrastructure, which is like a huge gap that's currently not
being moderated or also no solved.
And so I will be interested in first, what's your take on how
deep of a knowledge to employees going forward need to have an
order for them to apply it and how to second solve this?
I don't want to call it limbo, but at least I want to call it
responsibility loop that is not solved yet.
I think it's key, OK, what you are saying, I won't pretend to
have a clear solution. It's a clear problem, OK?
And it is even worse when you are considering that the fact
that people that are not technical but have the business,
business knowledge and the business insight will be going
forward, the more valuable. Because basically you need this
business knowledge to create powerful agentic system and and
the tech guys won't be able to create it without them.
So basically what you are saying this limbo as you call it, you
need to solve this to create powerful with the sprayed
agentic system all over the across the organization.
So one advice I would have, so there's been this theme with the
industry around forward deploy engineer.
What are forward deploy engineer?
They are engineer that are embedded within the business
teams to understand what they are doing and to create a
genetic system on top of it. I think this I would say
intellectual framework to put tech guys within business units,
so sales guys, marketing guys, whatever to understand what they
are doing, maybe educate them, unpower them, OK, when why they
are doing so is a very powerful idea.
And I think it's This is why open AI, Entropic and all of
these guys are basically are basically building up and
scaling such, such such system. It has been the first deployed
by Patanti, but it's a right now it's it is, it is more and more
we desperate. But what is changing now there
has been the release of cloud cohort by Entropic.
What people need to understand is that agentic coding, so
agents that allow you to code are accessible by anyone
basically because because they are coding, you don't need to
know I would say. I would say the art of coding
will disappear, but what will stay is system design.
Basically, you need to have a clear sense of what you are
doing basically to, to make make the the best out of it.
Myself, for example, on my personal usage, I must have, I
don't know, daily, I must have a 50 agent that are working for me
to code what I want. OK, I don't code anymore, but I
understand what I'm doing. I, I can catch errors, I can
Orient, reorient, I can come up with better ideas.
So it is a mixture between me and the agents.
And I think we need to educate, we need to arm power people to
leverage this technology. Because what I am observing here
on the field is that this combination of human plus agent
or coding agent, it creates 5050 time your productivity
basically, if not more so. So basically we need a clearer
organization. We need to have a clear program
of empowerment. Once we have the basis, that is
to say the basis is, is what we mentioned earlier, you need to
access. So you need to create gateway
for the agents to work on your data to work on your.
Once you have that, we will need to empower people to use it at
large scale. And this is by the way.
So what I'm saying is this is what I'm doing, OK?
I'm pushing for agentic solution, a really structural 1
and I try, I'm trying to empower people because what people need
to understand is that this modernization is impacting
everyone, OK? There is no, not in the world
maybe except at entropic organization that I'm using it
at large scale. So yeah, so it will transform.
I don't want to be a prophet, but the way we know our
workplace, so how we are doing business will change
considerably in the coming months.
OK. And not leaning into the profit
side of it too much, but still looking in the future, yeah.
What does enterprise AI look like for you in 2020, let's say
8 or 9? I mean, whatever long you want
to look into the future and how do we transition into this multi
agent ecosystem, organization, collaboration, what do you want
to call it? And also how specific, like how
would you envision of how being productive would look like in
that? Yeah, yeah, yeah.
So I will give you an image. I think most people will do what
I'm doing OK every morning you will have like a set of outcome
coming from your agent because my agent are working by night.
That's all OK. It's, it's practical and
basically all of day long you will switch between high impact
customer meetings because we, we, we will, it will stay OK as
a fact of going to town, having a nice lunch with your high
profile customer, etcetera. But what would be cool is that
everything that is around such high impact meetings will be
managed by agents. So before this meeting, I will
have prepped my meeting, your customer meeting with all the
data that I need. After the meeting, everything
that is around follow-ups, connecting with design partner,
connecting internally or externally will be managed by
agents, Gatner says. So I, I, I fetched up of course
for this podcast, they said that 50% of work decisions will be
made autonomously by agency Ki by 2028.
I think there is one condition is a governance layer is secure
and we have security etcetera that this number is will be
considerably more and maybe 50%. But would that end up in agents
talking to agents? Because if we would now have
lunch, let's say in Munich or your hometown Paris.
And then we met up, we had this great conversation and then each
of us, you know, returns to or the Skype translate it anyways.
And then my agent would send your agent a follow up of the
conversation and your agent would answer or what?
How would that look like? Of course, I mean, yeah, if I
it's already happening, OK, I can't I can't speak about the
detail, but myself I already built system where yes, of
course everything that is follow up understanding you like like
following the state, OK and updating the state, it's managed
by agents. So of course, I think everything
that is related to schedule, everything that is related to
updating the schedule maybe with new data or coordinating an
event. For example, recently I work on
such system, that is to say, maybe matching people with with
an event, event during an event you have multiple meetings,
multiple schedule and you want to perfectly personalized the
invite to the people you want to invite to what is happening
during the event. All of this can be managed by
agent. So yeah, and more books we have,
we had recently an experiment which is called the MUD.
So there has been a lot of fuse around it.
That is to say it's a social network for agent etcetera.
A lot of so a false promise. That is to say most of his
agents were prompted to do this. OK about the end of the world or
we will take over extra. So it was funny, OK to watch.
But what's demonstrate is that you can of course have like it's
an event based system. That is to say an agent is is
like subscribed to the to a flow of event and react to the change
of this event. So you can think all of the
enterprises system around this. That is to say myself, what I I
do normally is what I would follow my e-mail and react to
the e-mail I received by checking up the databases.
The news can do that perfectly. So basically takes the example
of emails and following up e-mail.
Currently, that's the future. Currently all of this stuff can
be automated. Once we stay, I think we will
have an explosion of software, OK, basically an explosion of ID
and an explosion of execution of those IDs.
Because what's what's this technology empower people is to
directly go from IDs to execution.
So what will happen I think is an explosion of of personalized
software, OK, that's a fit to your use cases, fit to your
problems, adapt to your situation, etcetera, etcetera.
If that's the case, doesn't then also the human side of the
connection and interaction become even more valuable and
also more appreciated? Yeah, yeah, yeah, yeah.
I think, I think what will stay is this is I impact, I impact
customer meeting or I impact partner meetings, but these
meetings will be so empowered. This meeting will will be like
the the last step of of a process where we will have
everything to decide or or do business or exactly.
So I think this technology will basically arm power to have much
more quality time gathering with people like Star star.
I don't navigate anymore on Internet.
OK, So people will will find it funny.
It's the only thing that I navigate are social media.
OK, because you can scrap it. You can't scrap it at large
scale, but everything that is related to website YouTube news,
everything can be analysed by agents and and everyday I
received like a newsletter of what I need to understand what I
need baby to to look up. I have synthesis across
analysis, etcetera. All of this time it is gain is
gain on on on on on being being more outdoor meeting people,
meeting customer extra. OK, so I think this
paradoxically and this is my optimistic vision, this
technology will allow to get more human less, less time, less
time spent on front of a screen and most time spent with real
human interaction where you create a trust where where you
haven't saw all of this. I would say I'm that sick
understanding everything that AI can do currently and I hope I
hope won't be as powerful as us, because I think it will be
really, really complicate to automate what we get.
The intuition, the personal feeling, the energy feeling
everything. All of this stuff is very would
be really really located for the.
Neuroscience side of. It exactly.
Since we're now come at the end of our of our episode, I do want
to like jump into some in between moments that I want to
have. And the first one that I put
towards you is what does Gentek AI feel like to you?
It's funny because so when I was a kid, I used to play a game
called StarCraft. So it's, it's really geeky.
But basically it's a game where you are commander and you
basically you lead units to gather resources to attacks the
opponent, etcetera. An agent TKI is like this, OK?
You have like units are managing, you are managing your
workforce basically, and you are managing your workforce maybe of
5040 agents. So it's it's it's required from
you strategic thinking, some sort of management, also
organization, etcetera. It's feel like this, but it's
feel like a game. But it's very fun, OK?
And what does trust in enterprise system require?
I think transparent failure mods, yes, I think basically you
need to have a transparency of what is happening and what it
fail. You need to precisely understand
why. And what is the biggest risk for
enterprises? I will be provocative but
staying at the current stage where you only have human in the
loop and calling it transformation.
And what? What should start-ups stop
doing? Building technology in search of
a problem. Start by with a problem.
Well, thank you for pushing ideas and boundaries and also
moving us into the future. Thank you.
I appreciate the conversation and all the insights that you
share. Thank you.
Thank you, Eva.