How AI Agent Swarms Might Be AI's Next Leap — With Guillaume de Saint-Marc

Channel: Alex Kantrowitz

Published at: 2026-04-13

YouTube video id: 6zZhNehawnI

Source: https://www.youtube.com/watch?v=6zZhNehawnI

What's going to help AI agents take the
leap from a promising technology to
something that gets stuff done? Let's
talk about it with Guillaume de
Saint-Marc, Vice President of
Engineering at Outshift by Cisco, in a
conversation brought to you by Outshift
by Cisco. Guillaume, great to see you.
Welcome to the show.
Hey Alex, thanks for having me.
Okay, so today we're going to talk all
about how agents work together and what
that might lead to as AI advances. But I
want to start here. I'm curious if you
saw what happened with Molt Book, this
group of agents that came together and
formed their own social network.
Uh it seems like they may have started
their own religion.
Um
what did you think when you saw that?
Did Were you scared or were you excited
about it? Well, that was fascinating. I
mean, we were certainly all all taking
the seat back, you know, in January when
this went viral and starting to eat
popcorn. We were like, "Where's this
going?"
And um but seriously, uh this is an
interesting uh engineering case study.
Um
at the time, you know, this was
skyrocketing in terms of number of
agents. Uh you know, went to a uh
million plus.
Uh
you know, more recently I see that they
start to try to identify agents a bit
more seriously, like backing by a real
human behind. So, we're more than like 2
uh few hundred K's.
But um all this on a single platform
from a pure
agentic connectivity standpoint worked.
And this was quite fascinating. Message
got through. Uh you know, agents could
find each other.
Uh but of course, so on one side there
was a lot of security issues that with,
you know, the uh cloud security platform
found a lot of vulnerabilities. The back
end actually was wide open. And so,
sadly, you know,
uh APIs uh credentials and and
personal email got uh stolen. But um on
the other side, um
the problem we rapidly diagnosed, uh and
it was not a surprise, is that uh the
spectacle was good, but um
those agents were really pattern
matching their way through trying social
media behavior. They were not really
um, uh,
doing proper collaboration. They were
giving us like a performing a theater of
collaboration. Um, so uh, there's no
shared state management, no
uh, governance layer, no mechanism for
agents to uh, really uh, coordinate on
anything meaningful. And um, and so we
we are but but we think there is a this
is still, you know, uh, very um, this is
heading towards a very interesting
direction. And we think we get there.
And these missing pieces uh, to enable a
group of agents to truly not just
connect but think together and go after
much more autonomously after uh,
you know, complex goals and more
advanced
uh, uh, missions. Uh, this is exactly
the kind of tech that we are building at
the moment.
>> Yeah, it's pretty impressive. So, 14
million comments on Multibook, 2.3
million posts, and about it seems like
200,000 verified agents going back and
forth. Uh, Yeah, 200,000 yeah, 100 100
K, yeah, exactly.
Yeah. And And the reason why I bring
this up is because
Outshift by Cisco is the part of Cisco,
and correct me if I'm wrong here, that
sort of thinks independently, tries to
get ahead of uh, what's going to happen
in the future. And the thing that you're
really focused on is what happens when
agents coordinate. I happen to think,
like after seeing Multibook, like that
made it real to me that you might end up
having these agent swarms
uh, actually playing a role in the
world, in the tech world, in the
business world, maybe the broader world
that we live in. And
you know, I thought it was like, you
know, us having a conversation about
what that's going to mean uh, was super
important now because we've now seen it
in action.
Yeah, I think there's a lot of
inspiration here for the for the
enterprise world. And uh, but we've
we've been at this for quite a while.
So, um when we really started focusing
on this, and this was more than 2 years
ago, we had this um strong um
thesis and vision that the intelligence
will come
not just from building ever smarter and
ever bigger and more powerful single
agents,
um but more uh through what we call
horizontal scaling. So, with distributed
systems, a lot of agents coming together
and working just like humans, just like
what we do. This is how we evolved over
time
across
uh um
um million uh you know
hundreds and thousands of years. And we
think that this is going to be the same
with agents, but much more uh rapidly
because they work at machine speed. And
so, we this emergence of you know, what
asymptotically will become super
intelligence has been uh fascinating for
us from the beginning. So, we we we we
we worked on this, and and last year we
um we released what we call the Internet
of Agent infrastructure. And the goal
was really first acknowledging that yes,
agents are workloads,
but um they also have attributes of
humans and users. So, they came across
the stack as weird entities, new types
of entities that needed a proper layer
on top of the cloud native layer to be
addressed.
And um and so, we worked on four main
functions. How can we connect agents?
How can we give them a strong identity?
How can we discover them? And how can we
observe them? And this is no big
surprise here. We're Cisco, so we
connect,
we secure, and we observe things. That's
what we do in in our business. But
applied to agents, there was a real need
for reimagining all these functions.
And um and we we to back all these
efforts, we've actually created an open
source project called Agency.
And um and this, you know, became a
Linux Foundation project with the
backing of a few large uh formative
members, uh Google, Oracle, um
Red Hat were with us
uh and Dell as well. And so, uh this is
This is out there. This is in the open.
This is being used. And we see we start
to see enterprise really moving uh to
multi-agent systems.
Um but as we did that,
um we also discovered that there were
limitations. So, just like the Mobot's
the Mobot example, it's it's it's great
to connect agents.
And this is useful. Uh you can achieve
stuff like this. You can achieve some um
what we call mass multi-agent systems
that can go after some pretty complex
tasks. And we've uh experimented with a
lot of use cases and stuff that we've
open sourced and stuff that we've
developed with uh design partners.
But uh but we've also started to see the
limit of it. And and the the limit is
really
basically if you have a a sort of a
predefined workflow. So, basically think
about a workflow like the the classic um
uh corporate workflow, you know, go step
1 2 3 4.
Um and if you replace uh uh the
different tasks, if you put agents
against it,
uh you can get to some pretty good
results.
But these are not the most interesting
missions. They're not the most
interesting autonomous sort of deep
agent deep multi-agent system uh mission
that we want to go after. Those more
interesting requires what we call a sort
of a self-forming collaboration between
and sometimes self-selection and
sometimes self-evolution of the mass
itself.
And for that, we realized that there was
all sorts of cognition issues that were
starting to rise like agents where could
communicate, but they couldn't really
sync together. And we uh we put our
finger on this, and this is what we are
working on now because we want to enable
this next wave of um innovation with
Argentic.
Wait hold on. You said self-forming. How
does this end up being a self-forming
entity? Yeah. So, self-forming is
actually when you think about it and
this is fascinating.
I'll take another you you mentioned Mole
Bot, I'll just, you know, pick up on
Open Clo. But if you look at Open Clo,
the way it's actually so Open Clo is um
let's put it this way, it's a it's a
state of the art agentic loop, right?
So, the agent can reason, but the way
it's doing its reasoning based on
memory, based on context, based on
skill, also soul and personality is
actually going to
spawn of agents
through reasoning and that is
self-forming. So, basically the
the set of agents and again, this is
just taking a small popular example with
Open Clo, but think about it happening
at a much bigger scale, you know, within
corporates with a much higher level of
security
and and um
um
higher level of security and higher
level of certification, if you want. And
and that's it. So,
the ability for this agentic system to
reason and to decide which agents they
need to bring in, that's why it's so
important to be able to discover agents.
That's why we have the directory feature
in in agency cuz that's one of the
fundamental building block. This you
know, look at the skills you need,
discover these agents and bring them
along in your mission
and task them with portion of the the
the the
the plan that you've just formed. So,
this is all happening in real time and
this is how so you see now the
difference between this and a sort of a
pre-formatted workflow. Workflow are
great because they are very
deterministic. They can be reassuring at
the beginning for enterprise because
they can be certified. But if you really
want to go after interesting mission,
you need this self-forming reasoning
aspect where actually at some point
between all these agents some some some
deeper cognition and reasoning
challenges are going to arise and I can
tell you a bit more about this if you
want.
Yeah, I mean I love this because this is
a real technology conversation. This is
sort of the cutting edge or the the
of where the technology will go. And so,
is it your perspective that
this is what we're going to see happen
is
agents will form their own self-form
their own swarms and go out and try to
do things?
Yeah, but you need to absolutely We
absolutely believe this is going to be
the case. Again, because
>> do you control that? How do you control
that? Because it seems like a path to
runaway AI.
Yeah, so so this is where you need again
and and your question is spot on.
Controlling this is part of the
cognition challenges that we've
identified because you need to put a lot
of
guardrails around this. And so, you can
control it at different level.
So, if you think about
the the the pure connectivity level, so
again, stuff which is out there and when
I say it's out there, I I just want to
make sure
folks listening to the podcast
understand this is very concrete. Go to
agency.org or you know, find the
corresponding git and you'll find a ton
of code. It's It's not concept, right?
You have a lot of code. We have example
application. We have something called
coffee agency. You can go there. It's a
you know, the equivalent of a stock
shop, you know, for Kubernetes. This is
how to get started and we exercise a
different agency function in a little
example application and then we have
more advanced examples there as well.
And so, to put things under control, you
need to you need to control
connectivity.
So, for that,
we have
First of all, we can very strictly
control
who's talking to who, right? Just like
in There is a fundamental And this is
where applying
networking vision to this technology is
so powerful because with networking, you
might be familiar with
techniques or technology called network
segmentation and micro-segmentation.
Like for instance, you make sure that
the the the laptop of the finance guy
cannot see the laptop of the HR guy,
cannot see the laptop of the you And you
some some some
servers
in this function, you know, are sort of
strictly segmented and cannot just
communicate randomly with anyone. So, we
do the same with agents. So, agents,
they usually like agents like to work in
groups, not just work one-to-one. And
so, we form
we form rooms of agents, a bit like a
WebEx room or a Slack room, if you want,
where people are exchanging. So, we are
creating rooms, and agents have to
communicate within these rooms. And
these rooms are designed, you know, to
address special tasks or special part of
the mission, and they don't necessarily
see the whole project. Um and so, a lot
of these techniques are enabled through
a technology called Stream that we have
in in Agency, which is really
all the agent transport and agent
network connectivity. Everything is
encrypted and all this. So, it's really
important. We are using a technology
called MLS, which is the same technology
you use for
collaboration platforms. So, like like
if an agent is going rogue, we can
revoke the agent, but it doesn't break
the room. All the other agents continue
to have access to the data that were
exchanged. You know, only this rogue
agent is now, you know, completely
outside and cannot access anything. So,
this is this this has been well suited.
And the last point I would say is
we also have something called T-BAC.
T-BAC is really important, and this ties
to agent's identity. So, when we welcome
an agent in the in the system,
again, the agent has been discovered
through through a pretty rigorous
way of assigning um cryptographically
signed what we call the agent cards, and
we work also very closely with Google on
this and the A2A group. We are very
active here. It's not just Agency. I
mean, we we're also teaming up with the
rest of the industry, of course, here.
Uh and T-BAC is cool because T-BAC is a
way to say, well, certain agents can
access certain tools, but not others.
And there is no reason that this agent
this particular agent will try to access
these tools, for instance. So, forever,
we
uh we ban it. But, what we are doing
something even more interesting is
when an agent is giving a particular
task, you know,
think about a micro task. The agent is
going to do 100 things for you, or one
of the sub-agent that has been postponed
is going to do 100 things for this
mission. But, at a particular point in
time, we ask this agent, for instance,
"Please check uh the uh currency
exchange rate between, whatever, euro
and dollar, right?" Something like this.
So, the agent is going to do and do
this. But, if the agent says, "Oh, in
order to do this, I also need to do a
transaction."
You're going to go like, "Red flag. Why
do you need a transaction?" Because I
just asked you to check, right? So, this
kind of semantic level verification is
absolutely something that we are doing.
Like, you can like an independent micro
agent, if you want, is going to come and
check that
uh the the kind of the the tools that an
agent is going to call are consistent
with with the task that the agent has
just been given.
And so,
I could I I could go on and and share
more example of guardrails, but this
gives you a bit of an idea of how we are
putting this under control in terms of
the product.
>> Okay.
Right, because I think these things
become most useful when you give them
access to the most data, but on the
other side, that is sort of where things
get kind of tricky. So, that'll be, I
think, the thing that the industry is
going to need to figure out is how to
combine that access with that sort of
safety or comfort that you would have,
because it's it's not simple.
No, you're you're so you're 100% right.
Uh spot on. And and this is um an agent
with no agency is useless.
Correct.
An agent with um
you know, too much agency can be
dangerous, uh
and especially if it goes out of
control. Uh this is a little bit what we
saw with uh again, Open Cloze is an
amazing piece of technology, but uh the
folks at this is, of course, security is
a big focus now for Open Cloze. Um but,
um at the beginning, it this was the
problem. It had a lot of agency, it
could do a lot of cool stuff, but it
could also go, you know, uh uh uh off
rails, right? Which is which is an
issue.
Um
But yeah, but but but there are also
other types of challenges when you try
to um
have a group of agents working together.
So,
um and and and this is what we call So,
this this is why we are building another
So, if you bear with me for a sec, Alex,
I'll I'll explain the layers that we are
building because there are two, so it's
relatively simple. So, think about it as
we don't want to reinvent the wheel. So,
everything is running on the good old
internet. That's already granted. On top
of this, you have the
um
really the um
the cloud native stack. The stack that
is, you know, used by all the cloud
native developers. This is how we've
developed all the applications that we
love and use today. All the SaaS, all
the mobile stuff has been developed on
this. So, that's the cloud native the
cloud native layer.
And so, what we've done with agency is
that we've said, "You know,
it's time to think about a new layer."
And by the way, to be very concrete,
uh we've also published a paper
extending the OSI model. So, like this,
you know,
most engineers will know the seven
layers of the OSI model, the IP stack,
uh and the the the the different
communication layers. And level seven is
really where the world has been living
for the past 15 years. Everything is
application level. All the modern
transport, you know, from quick to
HTTP, of course, and uh and all these
lives in layer seven.
And this is good, but we thought that
for Agoric, it was time to recognize
that two new layers are emerging. So,
one is what we call the syntactic layer.
So, that's really the layer to connect
the agents with each other. And this is
where you have protocols like A2A or
MCP,
which uh lives. And this is really what
where we've been focusing with agency.
And um per our discussion, recognizing
that this was absolutely needed but not
enough, now we're adding a final, and
this is the last one, we hope, layer on
top, which is layer nine, and this is
the semantic layer. So, this is where we
really actually care about um, what the
message is about. You know, usually when
you when you transport IP packets, who
cares what's in the box, right? Like IP
packets, you know, content, content. But
with layer nine, we're starting to form
headers,
which are really um, giving you
indication about what this message is
about, what it is saying. Is this like
an agent trying to share um, um,
an intent with another agent? Is it just
sharing a knowledge? Is it trying to
delegate a task? And so, this is this
these layer nine protocols are the
protocols we are currently building to
be able to um, enable these um, the
agents to sync together and to have the
proper cognition and cognitive behaviors
that we are expecting from them. Okay.
And and I'll just note your agency is
spelled a little differently than the
standard spelling, A G N T C Y. We'll
link it in the in the show notes.
>> No vowels, no vowels, yes. Just A at the
beginning, Y at the end, and no vowels
in the in between, yes. It's it's a good
point because folks can struggle to
finding it otherwise, yes. Yeah, okay.
We'll we'll definitely link it. Um, can
you bring it a little bit more concrete
for us in terms of
well, what do you expect a swarm of
agents to be able to do that a single
agent couldn't necessarily do? You know,
when you think about this working in an
ideal form, what does it look like?
Yeah, that's a great question. Um, so,
we think that the a good a simple way to
think about it is when you think about
um, cross-domain, cross-functional
agents.
Um,
I'll take an example. Um, you want um,
you want to have an agentic solution to,
for instance, um, resolve
severe outage on your IT infrastructure.
Argenti cops by the way is a great and
this is obviously as as algae but also
as Cisco. This is a big area of
application for us in our you know,
backyard.
Argenti operations of IT systems. That's
what we do.
And we we see a massive impact of in
terms of productivity and savings that
Argenti can bring. So take the example.
You want to have an Argenti system which
is capable of
bracing bringing things back to normal
when you have a
pretty bad IT crisis.
Well,
this is this is a complex problem.
You have the SRE dimension of it. So
basically that the platform you know, is
you know, your different clusters, your
different Kubernetes containers.
And that's the
the site reliability engineer that is
responsible for keeping the
system.
>> By the way, just a quick
side note on this one. We entirely So in
my team, we've actually built our own
Argenti
SRE system. It's a multi-agent system.
It's called Cape c a i p e.
And we've entirely open sourced it with
from the
with a group called
Canoe c n o e which is basically a group
of you know, you have folks from AWS and
Adobe and us you know, and and many
other large enterprise
you know, progressing the state of the
art on on SRE and and platform
engineering. So very concrete example
I'm giving here. So you need this you
need this SRE agent because of course
you need to be able to act on the the
platform. You need a security agent
because there might be a security
dimension to your problem. You need a
ton of observability agents as well. And
this cannot be the same agents. These
are entire domains of their own. Like
typically your observability agent will
come from Splunk if you're a Splunk
customer because that's clearly the best
team capable of giving you your
observability agent.
Um the security agent will come from
Cisco or from another security provider.
Your SRE agent might come from uh one of
your SRE provider or maybe you've
designed it yourself or maybe you're
using Cape. And uh you can also have
other agents like um crisis
communication agents because you might
have to talk about this or you might
have to communicate to your customers
and to the world about your outage. And
so, already you see that with these
five, six agents I just mentioned, you
need to have coordination between these
agents and that's the only way you're
going to achieve this mission.
Um and so, this is the kind just to give
you this concrete example. This is the
kind This is the level of ambition we
have. So, call it super intelligence or
just regular intelligence. Um
a lot of the experiments we do indicates
that uh we we'll go from hours,
sometimes days of trying to put
situation like this under control to
hopefully just a few minutes. So, this
is going to be very significant in the
And And you know, another agent will
have like will be like the agent capable
of finding
troubleshooting like finding what we
call the root cause analysis. Uh you
know, doing the root cause analysis of
of the problem.
Uh we had some amazing results on this.
Literally going from putting experts in
a room for 3 days to just a few minutes.
Uh so so
Anyway, I hope this is making it very
concrete why why we are
Just want to be very clear.
We have good reasons, but we are not
fully there yet. You want to connect
agents, you want to follow workflows.
You Yeah, we already have a lot of good
stuff.
You want to put a complex cell phone
because in this case of the
uh the the IT outage um solution,
the the the team of agent is going to
create a plan which depends on the
outage. There's no Of course, they can
go through playbooks, but it this is too
rigid. You know, this might be something
new we've never seen. I mean, we have
new attacks or new outages that we've
never seen before every day in in the
news, right? So, you see how the
self-forming, the real reasoning, and
the cognitive collaboration of the
agencies needed. And uh and so this is
what we hope to enable. But, there are
challenges. There are challenges to
uh to enable something like this.
Guillaume, let me ask you. This is
something I've been wondering for a
while. Um
when we think about agent swarms, right?
Like there's a coordinator agent and
there's like the SRE agent, all these
different types of agents. Um
is that different underlying technology
or is it like the same agent or the
sub-agent the same agent just with like
a different prompt task to look for
something else? Like what's the
differentiation between agents there?
Yeah, that's a that's a Well, so it
depends. Um it's So, it's
So,
if you look at something like um uh l-
like I would say a a a simple
yet
powerful
um agentic solution like OpenGlue,
they're kind of the same, right? You can
change the model behind, you can give it
a different prompt, some maybe access to
different skills, but they're roughly
the same type of pattern. When you go
back to my example,
this can be the the diversity of how
agents have been uh coded, created can
be much wider because they come from
completely different domains, different
company um
there's little chance that the one of um
like a a Cisco agent or Microsoft agent
or Salesforce agent, which you might
need to bring together to solve an
enterprise, you know, uh
cross-functional use case
will be using th- they won't be the
same. They won't just be a a prompt. Uh
they will be much more than this. They
will use memory, they will use their
specific guardrails, they might use uh
some of the what we call the
uh uh cognition engine, so accelerators
for um
you know, how these agents can
collaborate. And so they will be very
different. So, um
to answer your question precisely,
um as complexity is growing and as we
again, as I said before, we asymptoted
asymptotically start to progress towards
something we can call super
intelligence, it's going to be very
heterogeneous, very different types of
agents, different vendors, different
clouds, different technical frameworks
will still have to collaborate together.
And am I right in thinking that if you
have like one agent in the stack that's
more multi-purpose, maybe underlying
that agent you have one of these bigger
generalized models, whereas like if you
have one with just one task, one task,
you can have it run with a lighter model
so the compute and the cost is not as
intensive. Absolutely. Uh so I mentioned
the I'm I'm going to um this is stuff
that we are going to share soon, but um
like for the the T-Baq uh functionality,
the semantic T-Baq, for now we are using
um um uh you know, picture model. So
connect the model you want to to
actually power well the solution.
Um but we're also working on a on a
small language model uh which is going
to make it um you know, much much
smaller, tiny, that's why it's called
small language model that will power our
feature like this. And this is important
because when you think about it,
T-Baq is something that you bring to uh
uh to agents typically through a sidecar
um uh to each So each agent has a
sidecar which is controlling any
networking communication with this agent
and which is applying
policies, which is applying semantic
verification. What we again, we call
them cognition engines.
And uh so basically it means that each
time an agent is going to uh call a
tool, you potentially have to
generate one more call to an LLM. And if
this LLM is expensive, this can start to
exponentially cost you a fortune uh to
apply this level of sec
So, at this point having small language
models for these routine tasks, which
are highly specialized and as efficient
as a generalist model, but they can only
do this but very well, is economically
super important. And uh and and we are
highly conscious of that. And otherwise
will require the security, the
observability, the explainability, but
also the control of the cost on these
multi-agent systems. Well, let me ask
you this. You have mentioned a couple
times that you're open-sourcing some of
the projects that you're working on. And
I'm I'm happy to see that, but I'm also
curious why you're doing it because, you
know, my perception is maybe that uh
stuff that you're working on is
something you want to keep in house to
give Cisco an edge
uh over the competition. And yet here
you go open-sourcing it. So, talk
through the logic there.
Yeah, well, I'm glad you asked because
so we we think this, you know, what
what's in front of us is
is is really a challenge not just for
Cisco, but for the entire ecosystem.
By the way, and and
if you see if you go back to the root of
the internet, the internet has been
designed back at the time as an open and
interoperable system.
And we've noticed that there is a small
thing called the digital economy that
was
that emerged on top of that. And so
we've been obsessed by reproducing this
model. We saw a genetic coming and this
was so profound that we're like,
"This cannot just be
believing in walled gardens. This has to
be
uh
based on an open and interoperable
foundation." That's why we call it the
internet of agent, the internet of
cognition.
And honestly, these are complex topics.
So, uh we're happy to contribute. We
have a lot of ideas, but
no company can do this alone. I mean, we
need to do this uh as an ecosystem. And
this is the way to maximize uh the value
for the entire ecosystem, not just for a
few players. So, that's why we are doing
this. And not too worried about Cisco
differentiating because
uh these days you also differentiate by
velocity, and we have
you know, so we we we focus on
innovation velocity, which our product
teams are doing a lot.
Uh and our role is to make sure that um
some of these relevant technology goes
as fast as possible to to our peers in
Cisco who can Splunk, AI defense, other
teams that we are working with.
It doesn't mean that we are open source
necessarily 100% of what we do. So, of
course we can always keep a few things
like um okay, this is you know, this
piece of tech we can
keep in tow and and only graduate
internally, but I would say that I don't
know, probably more than 80% of what we
do needs to be open source because we
actually believe in this importance of
open and interoperable.
Yeah, and that velocity point is well
taken. Things are moving fast. Yeah, the
things are moving moving really fast,
and
that's why we we're we're in the middle
of conducting a lot of um experiments.
We hope to
So, for now, just to be very concrete,
the internet of agent is out there.
Stuff is getting into production. It's
um as I mentioned, you know, it's it's
tied to the hip by the hip to A2A, and
so the the fourth piece of technology is
the observability already available
through Splunk, and we've open sourced a
lot of stuff, and there are many players
doing this. So, observability, the
connectivity, the identity, and the
discovery of agents, all of these
technologies are usable today and out
there.
The internet of cognition, we are still
working on this, right? It It's It's So,
we've
um
we've published um a white paper in in
January
uh with the with the vision,
uh and we are working hard on the
architecture. Um we are going to we are
going to release some code very soon, a
bit later in in April.
Uh
this will be just a a sort of a humble
beginning trying starting to share
tools and examples that people can take
inspiration from.
Uh, and we are going to keep pushing.
One thing which we are doing at the
moment in order to validate our
architecture
is to conduct a lot of experiments and
see when we put swarm of agents
together, see where it starts to derail.
Uh, so we have a a taxonomy if you want
of
seven or eight cognition issues which um
happen on a
recurring basis which we keep seeing
popping, you know, across these
multi-agents and we are trying to tackle
them one by one and we're trying to
build an architecture which can
alleviate or
remove these cognition issues
amongst agents in order to to be able to
make them function super well. All
right, Guillaume. So, if people are
interested in learning more and getting
involved, where should they go? Well,
that's
pretty straightforward, Alex. So, we
have this algocisco.com
website where we keep all the news and
look for the Internet of Cognition
subpage because here we're just dropping
code
and links to code that is
going to show how we can have Internet
of Cognition
really like initial infrastructure helps
you coordinate
more complex mission across across
agents of different sorts.
We have white papers. We have also links
to more academic paper which we have
started to publish on the architecture
and last but not least we have a pretty
cool also demo about all the concepts
that I've explained on the video today.
So,
go check that. There's a ton to keep you
busy and keep an eye on it because we
are going to roll out more content in
the next few months. Awesome. Well, I'm
really looking forward to following the
journey. Guillaume, thanks so much for
sharing everything today. Appreciate it.
Thank you, Alex. And please join us
because none of that can be done by
ourselves. So, all the behind all the
open source we do, we have working
groups which are open for you to join,
for you to contribute, and have fun with
us.
Terrific. Well, thank you, Guillaume.
And thank you, everybody, for watching.
We'll be back on the channel with
another video later this week.