AI Copilots for Tech Architecture: The Highest-ROI Use Case You’re Not Building — Boris B., Catio

Channel: aiDotEngineer

Published at: 2025-11-24

YouTube video id: QRWdapxMdSY

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

Hi, I'm Boris Bogatin, CEO and
co-founder of Kato.io.
>> Hi, I'm Tufi Pubz. I'm CTO and
co-founder also at CADO.
>> Today we're here to talk about AI
co-pilots for tech architecture, the
highest ROI capability you're not yet
using. A topic that's been near and dear
to our heart. Over the last two years, I
would say coding co-pilots have become
truly table stakes. You know, it's
interesting because, you know, you take
it back three, four years ago. I know
Tufik and I talk about this a lot. to
his days back when he was the VP at
Splunk a lot of the you know hot shot
developers would always talk about how
you know coding co-pilots would never be
able to kind of supplement them
>> right to
>> yeah it would never work yeah
>> never work right because how could you
and now coding co-pilots are helping us
tremendously multiply productivity
output and you know if we look at the
whole cycle as you can see on the slide
here you know the full life cycle of
software development has been so well
you know uh situated and and and served
with tooling from software project
management to execution to operations
Splunk and Data Dog. Today the software
life cycle self-development life cycle
is filled with tooling coding co-pilots
multiplying the productivity and we're
excited about that which we should be.
But when you step back you step back and
you ask the question you know is there
something missing and something yet not
addressed? Because isn't the highest
leverage co-pilot the one that we're
really not using yet the architecture
co-pilot? Why architecture? At the end
of the day, architecture is where ROI is
one or lowest. If you're going into the
wrong direction with a lot of coding
output, are you not going to get to poor
code, poor results, and a lot of redo
and tech debt versus moving truly into
the right architectural direction?
To us, architecture decisions is what
drives things like nine figure spends um
through business objectives and and and
how tech fuels them instead of slowing
them down. How you can stay ahead and
bestin-class versus drown in tech debt
and always playing catch-up. That's
really at the heart of you know why
we've come together here um around this
topic and uh we're seeing this across
the board with a number of stakeholders
that we'll talk about today. Today's
reality a lot of the work is managed
this with spreadsheets tribal knowledge
gut instinct it's always been done by
very smart folks CTO's and architects
and increasingly delegated in shift flat
fashion to developers and it's fantastic
to see that the whole organic process we
love it but we we we've always thought
there's got to be a better way and
especially in a day of AI there's got to
be a better way so today we want to walk
through the three critical challenges
that are keeping leaders up at night
that we hear day in and day out and how
they're being solved and what that
future looks like in closed door CTO
dinners and you know our work with
enterprises and growth stage companies
the like we keep hearing the same pain
points I know you've been in the weeds
on this what are the top three things
you keep hearing from architecture
leaders
>> yeah that are keeping him up at night
>> oh great so so based on a lot of
conversations we've had and actually on
my own experiences as an architect and a
CTO long-term CTO in in many companies
there's at least three big challenges
that we typically encounter counter. Um,
the first one is visibility. So, as your
tech estate grows, you start to fly
blind across your landscape. Excuse the
mixed metaphor here. I like these
metaphors, but you know, you start
flying blind across that landscape and
and it's really hard to kind of gauge
where you are or or or to make real
plans. So, that lack of visibility is
one of the biggest issues. The other one
is having ROI tied and databbacked path
forward. you know, knowing where to
focus, what to prioritize, and how to
defend your decisions in a way that can
be backed up by data is really it's
always been a challenge. I mean, I I sit
on boards or with other executives, you
know, at startups and at big companies
and the question is always well, you
know, I ask for things or I'm ask for
stuff and it's hard to always to to have
a good answer that is data back, right?
So, so how do you do that and especially
that is tied to ROI because at the end
of the day you know how do we spend how
do we manage our spend. The third one
though is some form of autonomous
guidance now that a lot of organization
are shifting left and and delegating
more and more decision making to the to
and empowering the developers which is a
great thing. figuring out how to guide
them and equip them with expertise um
you know at scale is the third big issue
that we're that we're constantly facing
these days. So um and the main reason
for these issues is uh you know there's
there's no dependable live holistic map
of our services or dependencies and
drift how things change over time really
there's no baseline from us to go from.
So as a consequence you get slow
defensive decisions. You got redundant
spend you know that you can't justify.
You know you got risk that's not
properly managed. You know you're
planning you know you mentioned Boris
about you know tribal knowledge and so
on. You're planning basically by opinion
instead of planning by data. So what we
really need is some kind of live
visibility that captured all all that
messiness in our system, all that
knowledge and the shifting dependency in
essence like a sh like you know the
developers have a shared code book a
shared current reality for us for for
our working systems you know because
without it really you're making
sometimes multi-million dollar bets
without knowing what you already own.
and and you know we've seen that
actually in uh in some of the prospects
some of the people we're talking to
behind closed doors. So
>> absolutely.
>> Yeah. Yeah. So to continue the analogy I
know I'm mixing metaphors using
analogies here but you know to continue
the analogy if you want to chart a
fruitful path forward uh you know what
you really need is an accurate
up-to-date map. So when you're charting
path forward you need a map. You need
some kind of living architecture map
that updates itself as your system
evolves. So that's kind of like one of
the major major things that we're
looking for.
>> Absolutely. Thanks, Tik. No, completely.
So you have visibility.
>> Yeah.
>> But now what? How do you prioritize? I
know there's a lot of scarce resources.
Business wants to achieve some, you
know, very important objectives, rapid
growth.
>> Yeah.
>> And everyone thinks their project is the
key to success,
>> but without the good proof. How do you
reconcile that?
>> Yeah. My project is is the critical
thing. You have to do it.
>> Of course. Obviously. Okay. So, so what
you're asking me is how can I get expert
ranked actions that are tied to business
impact because at the end that's what
that's what matter like cost performance
risk time to value all these things that
matter to the business right so it's not
just what should I do next or sh what
should we know and or whose project is
in favor right it's what should we do
next given our constraints our existing
investment and our strategic goals
that's real question that's really what
you should be focusing on right
>> completely I mean I I think this is what
we're hearing where the challenge really
lies. And it's always what I always love
is getting into those dinners that we're
doing and podcasts and the whole
architecture deconstructed movement and
asking those questions. Oh, that nice
t-shirt. Asking those questions. Asking
those questions openly. You always, you
know, kind of I'm always surprised by
the, you know, kind of the the the
honesty and and intimacy of the
responses that are really almost
demarried from what you expect. You're
expecting certain things like I want
perfection here or something else.
people are like I'm just trying to make
sure that you know business understands
what we're doing is important and
allocating budget to us and we're able
to drive the business forward really
care and not like kind of poke at a
bunch of random directions. So anyway,
so I I totally get this one and how do
we tell what is the right architecture?
How do we prioritize the work? What are
the metrics? What insights do we use to
know this kind of to achieve that kind
of impact? Right.
>> Yeah, absolutely. So you know you have
to have a system of recommendations. The
reason these recommendations really to
fulfill what you're talking about must
be explainable and traceable. In essence
why is this recommendation valid? Where
is it coming from? What is the expected
impact of it? And then you know what are
the measurable outcomes against some our
key objectives. Right? So what this
results is is a road map where every
initiative is clearly scored for impact
with the ROI justified and kind of the
business objectives and best practices
are all taken into account. That's
really what it comes down to
>> completely. And if I may just jump in
for a second course to me it seems
speaking about this seems like an almost
complete no-brainer. Why would you ever
want to start coding and developing
software until you have this answer?
Because if you answer this then
everything from there that's true
productivity. Get more lines of code out
that's great because now you know you're
coding in the right direction versus the
wrong direction.
>> Totally. It's the old you know ready
fire aim joke. You know [laughter]
>> you don't want that. You don't want to
do that. Right. So so it's it's the same
it's the same thing here. Right. So this
is even more critical these days though
to your point Boris because this shift
left promise which empowers developers
to make more decisions
>> has a flip side a little bit of darker
side which is that architecture
expertise and standards are not scaling
they didn't scale with that empowerment
right so developers are making
architectural choices whether you like
it or not and then the architectural
gills or the enterprise architecture
team whatever they review they just
don't scale effectively to that so the
question is how do you guide them
without being a bottleneck? Right.
That's that's a key question there in in
enterprises, right?
>> You know, and we we hear it all the
time, right?
>> Yeah. Absolutely.
>> We hear you hear teams saying, you know,
yes, it's difficult. you know, we have
all the presentations, we have all the
strategies, we get together every two
weeks and, you know, we hear crickets.
We're we're we're talking to everyone
and everyone is kind of trying to
absorb, but ultimately we get it because
they're trying to build features and
ship to business needs and ship fast and
their features have nothing to do with
our standards. They're trying to fit
their specific, you know, uh,
capabilities and how do they kind of
architecturally map that to the baseline
that we want.
>> That's right.
>> What's needed, right? What's needed are
tailor fit designs that are suited for
the developers co-pilots that can give
them in a kind of conversational
guidance ongoing guidance but all of
this I mean I know it sounds magical but
all of this with policy and guidance
built in so it's all policy and guidance
aware right and it's embedded in
developer workflow that seems like the
right answer
>> we'll talk about whether that's
achievable but that seems like the right
answer right yeah
>> and you know the governance paradox is
all about like autonomy without
alignment creates chaos
and gates without autonomy kills
productivity. And we know that that's
true. And so how do you reconcile,
right? We want to get Yeah. We want to
get developers to get that expert
guidance, generate designs that are
compliant, and stay aligned to strategy
so they're not waiting and they have
built-in alignment uh built in. Exactly.
>> Right.
>> Absolutely.
>> Well, let's let's shift now to a little
bit of how do we solve this, right? So
we talked about these three challenges
really important. Let's address how we
really kind of can think about them most
effectively. What are those three
pillars that make a true architecture
co-pilot possible and what it takes to
kind of accomplish them? Go ahead.
>> Yeah, absolutely. So, Boris, as you
know, you and I, Boris and I have been
thinking about this for quite some time
and and we've developed this kind of
these three pillars that are really
really important that together hold up
this whole foundation, this whole
business of architecture, right? So, the
first one is what we call stacks. You
know, it's your live visibility layer.
Remember I talked about the map earlier
having an updated up-to-date map if you
want to chart a course. So in essence
being able to ingest data across clouds
across Kubernetes services across
logging platforms you know building
model dependencies drift and change over
time and then maintaining this kind of
living architecture in form of a digital
twin. So you get all that data from
everywhere and then you fit it into this
build together this digital twin of your
deployment your architecture and a true
system model that reflects the reality
not what's in your wiki or not it's what
you have as opposed to what you think
you have right that's really the first
pillar having that that map that live
visibility map
>> that makes sense
>> yeah at the end of the day if you don't
understand what's this all about what do
you try and drive to where do you where
is the pot going right um you won't
really be able to get there and and in
that context you have to be able to
curate those business objectives those
requirements the standards and strategy
and and be able to kind of couple that
together
>> absolutely
>> into a context that the AI can leverage
in order to make very informed and
tailor fit recommendations with
expertise you know very custom fit to
the specific you know business
objectives and workspace objectives
specific team objectives they're trying
to serve right does that is that kind
Yeah, absolutely. So now you know this
this is where I mean we mentioned AI a
couple of times but this is kind of
essential. I mean one of the major goals
is to pri these kind of data back you
know best practices uh ROI based
recommendations right and especially
when it comes to architecture you know
not to not to kind of minimize the
amount of work that takes to do coding
copilot but architecture is yet an an a
higher level a higher degree higher
order of magnitude in terms of uh
complexity. So, so this is a really hard
problem and it's it's a um you know the
typical problem that you use what's
called you know uh distributed problem
solving because it's not a oneshot deal.
It is a problem that where everything is
interconnected right so you have to
break out all the dependencies and then
attack them and then and then work
together to actually come up to some
kind of recommendation that is global in
context right so this is a typical
distributed problem solving thing and
this is where you know this is perfect
so a type of solution for multi- aent
systems right so we've you know if you
look at how multi- aent system work if
you build agents that actually focus on
various parts of the problem and then
they collaborate towards a solution.
That's really kind of one of the best
ways to solve this kind of complex
problem. Right now a multi- aent system
right now today rely on large language
models LLM right and and LLMs have read
practically every every best practice
every architecture book and so on. So
they have a lot of intrinsic knowledge
that you can leverage. But eventually if
you think about the evolution of how AI
could go in the architectural space, we
can start thinking about maybe large
architectural models opposed to large
language models and then beyond that
some kind of true simulation of your
environment, you know, some kind of
system behavior modeling so that you can
actually try different scenarios and
maybe simulate different things so you
can look at the impact before making an
actual decision. So that's kind of where
we see the evolution of this
architectural AI going. I mean, we're
not there yet, but but that's actually
the the path forward for us as an AI
community for architecture. And Tiff,
you know, what I love about the notion
of multi- aent systems is that
ultimately, you know, in our
exploration, you know, when we try to
think about what's the right way, what's
the best way, you know, it's it's
amazing to to be able to step back and
say, well, listen, all this stuff that
we're doing as human teams isn't wrong.
It's a you know we perfected this art
with very you know you know high
aptitude and and and care and so the
process of design reviews is an
important process and it's a very
effective process except that it doesn't
leverage the right amounts of data and
we wanted to kind of be able to leverage
computational intensity that's maybe
higher and that's what we're trying to
do with multi- aent system isn't it just
replicate human processes effectively
with AI right
>> yeah in essence yeah taking that and and
and expanding it at scale using these
agents that and function like 24/7, you
know, at scale, right? Yeah.
>> Absolutely. Absolutely. No, that's
amazing. And look at the outcome is ROI
ranked explainable recommendations that
truly understand your tech and
objectives and act as that trusted
adviser across your tech estate proving
clear trade-offs across cost,
performance, risk, and time and help
prioritity of the road map. And what I
think what I'm really excited about in
this context is what we hear from
customers. What we hear from customers
when they think about architecture
co-pilots and they say that you know
what what what's really going to move
the needle in such a dramatic way is
when you go from you know even the best
practices that are good and are really
important to highlight but they're a
little bit more straightforward like
migrating from GP2 to GP3 to when you go
and you really understand the
intricacies of the overall architecture
and then the data pipeline can be
streamlined for next efficiencies on
reusability across a variety of
applications or other architecture
patterns that truly move cost and
performance needles forward. That's when
you get so much bang for the buck
>> and yeah and it's tied to an ROI and
it's tied to impact and there's a clear
traceability as we said before. So
that's I mean you take that to your
board or to your executive meetings
whatever and it's there. There's there's
no controversy around it, right? That's
perfect. Yeah. Um you know so that's
good. Now there's a third pillar.
Remember there's three pillars, Boris.
We don't want the thing to topple down,
you know. [laughter]
The third pillar is having some kind of
conversational architectural agent. This
is where the world is moving to this
conversational mode of interacting with
any system that you have. So interacting
with your architectural through a
conversational agent is is critical for
us as an AI community to move forward.
So it allows us to embed you know tailor
fit designs guidance and expert QA Q&A
into the into the workflow right. So
this achieves two goals you know allows
developers and architects and you know
anybody for that matter uh as a matter
of fact you know to answer questions
about the architecture to ask questions
and then be able to get answers about
their architecture. And the second thing
you know um and it gives you the
developers architects expert advice on
optimizing and and the refactoring the
architecture. So that having that
knowledge in a conversational agent is
really really critical. It also helps
developers by you know the next step
would be by generating designs for their
features giving a set of requirements
like PRD and knowing all the governance
and controls and guidance that say the
architecture team or the chief architect
or or whoever has put together they're
built in into that agent. So whatever
designs are given actually follow this
guidance intrinsically. Right. That's
really really critical. Right.
>> Absolutely. And Tufik, you know, you
said it earlier in the challenge
category. I want to tie that back here.
We talked a little a lot about the
solutions impacting leadership and
impacting ability to steer the ship,
right? That the overall tech estate. But
the reality is is that like we talked
about it's shift left. It's developers
that are really steering that tech
estate ultimately. And this is that
point, right? How do you translate that
top level guidance that visibility and
strategic road mapping to embed that
across day-to-day workflows that
developers are facing? And this is
exactly it. You know, I think the other
thing that's really powerful here is
that, you know, we want to be able to
see the architecture review process
change, right? You want to change from
having these architecture guild style
like once every two weeks kind of
reviews that are very merit worthy but
very hard to execute to where that
architecture review process is actually
proactively baked in. Like the beautiful
thing about AI is that it allows us to
get alignment by design. Right? If AI is
able to bake in that architecture
guidance into every single piece of AI
advice that it's giving to developers,
isn't that the amazing answer which is
tailor fit for developers with guidance
already baked in? And we have that
opportunity. We can set the AI context.
We can set the AI training and narrative
based on the leadership's imperatives,
but yet again tailor fit to the specific
context that the developers need
answered for them. Right? And this is
how you scale your architecture guild or
your enterprise architecture team,
right? This is how they scale. They
scale through the guidance they give to
that AI, right? Perfect. That's it.
>> Yeah.
>> And then we can change the paradigm,
right? We can change the review role
from being, you know, kind of trying to
figure out if standards are being met to
knowing the standards are met by by
design.
>> By default, by design. Yeah.
>> Yeah. And instead now, you know, we talk
a lot about like is AI going to take our
jobs, right? Instead to actually being
able to do more. Now we're talking about
productivity. Now we're talking about
strategic, you know, multipliers because
now instead of doing those mundane
things in the past, AI is solving that
we can focus on strategy. How do we
solve hard problems with our development
teams? How do we actually move the
needle forward in a way we never had
time before? Because we were always
mired down into how do we just make it
like fit the designs that that the
standards that we need, right?
>> Yeah. Exactly.
>> Yeah.
>> So to why don't you tell us a little
more about how do you bring this all
together in this context?
>> So here's how it works. I mean in our
minds at least end to end right the
first step is to ingest and understand
these messy systems right so you're
getting data from everywhere your
systems are messy every system is messy
I mean if you say your system is not
messy I don't think it's true so you
take that data and you normalize it to a
live model this digital twin that we
talk about so now you have it normalized
in in a in a way that you can look at
you can introspect you can you can
navigate and so on so and so so having
that. So now that you have that, the
second step is to kind of align yourself
and and have some kind of align and
advise strategy. So you have your goals,
you have your requirements, you have
your context as a company, right? You
know, my ideal in this industry, my I'm
in a in a hyperrowth phase or what have
you. So all these things together come
in together and then what you need is a
is a ranked recommendation set with some
projected impact on cost performance ROI
whatever metric that you want that's
really important for you as a company as
your context right so that's the second
thing the third thing is you know having
some kind of guideline as we were just
talking about intrinsic you know
intrinsic governance into these
guidelines these these designs so
generate designs answer what if in real
time and enforce standards in the
workflow. You don't want your developers
or architects to go to another tool do
something else and then come back. It's
part becomes part of the workflow,
right? And then you know you know how do
you manage things? You can't you can't
manage what you don't measure, right? So
eventually the last step is be able to
track these decisions, verify your
outcomes and then continuously improve
on it. So these are kind of the four
steps that we see as getting to this
changing the paradigm of how
architecture is done. Absolutely. And
two, you know, it's funny. I I know
you're you're a gay way with the jokes,
but you know, ready, fire, aim, right? I
mean, in the context of ready, fire,
aim, you know, isn't the right answer
then ultimately if this is the way to
aim, then doesn't this ultimately, you
know, seamlessly get interconnected to
our coding co-pilots. So then you can
fire, you can aim with with an
architecture co-pilot and then right
away, right from there, you fire with
the coding co-pilots. And now you've hit
productivity, right?
>> Absolutely. That's a great concept. I
can see a world where, you know, the
agents, the architecture agents are
talking to the coding agents, right?
>> 100%.
>> And you're just there to guide them,
make sure they're okay, they're doing
the right thing to corre correct course
and so on and give them the directives,
right? Yeah, that's coming.
>> Absolutely. Absolutely. So you know at
the end of the day you know what I think
we see is a hub for architecture and
tech decision-m being a really essential
part of the software development cycle
for these for these kind of you know
kind of aim imperatives right it's a hub
that transforms how companies plan build
evolve their tech estate and then
execute software on the back of it not
just writing more lines of code for the
sake of it right it unlocks orwide
clarity and faster decision cycles
ability to strategically roadmap so that
your roadap apps are truly tied to
highest impacts on your business
objectives fully equipping the tech
world to execute with expertise. Two
shifts left enablement and and outcomes
that don't just you know scale but to
reduce quality that scale and
dramatically improve productivity across
the board with guidance baked in and
reframes co-pilots really from
productivity tools to to yet a new
dimension. You know productivity is nice
but yet a new dimension strategic levers
for the business. We all know that
techdriven is the paradigm for how we're
moving industry forward. Well, this is a
true new frontier for how we can move um
things forward even further
competitively for competitive advantage
perspective staying best-in-class with
architecture copas setting setting up to
have true strategic levers in our tech
stacks. Absolutely.
>> So the companies that get this right, I
do believe that will be the ones that
stay modern, agile, and ahead. And
others that don't are going to be buried
in legacy and debt just like we're
seeing with coding co-pilots. Companies
that are not embracing it fast enough
finding themselves on the outside.
>> We are we are as an example, right?
We're fully on with the coding co-pilots
and it's helping us a lot. We've
written, you know, Boris and I have
written and the team have written some
LinkedIn articles and blog posts about
that how effective it's been for us.
Absolutely. Yeah.
>> Amazing. Well, and so just to wrap this
up, you know, too quickly, where should
where should leaders start?
>> Yeah. Well, do everything at the same
time or actually, you know, you start
small and like scale little by little
deliberately. So for example, pick a
portfolio area and get visibility in
that portfolio area like build you know
get get that you know digital twin built
on that particular area. Generate
recommendations in that particular uh
start small tie to business outcomes to
specific business outcomes in that area
and then start piloting some autonomous
guidance with one team. You know you
don't want to do this throughout the
whole company all the time. do it step
by step, right? And then scale little by
little to the full hub once you've
gotten ROI and you've proven that this
tool, this new tool because there going
to be maybe some resistance at first or
some skepticism, of course. I mean,
architects, CTO's, developers are all
skeptics by nature, right? So, prove out
the ROI first before you start scaling
to the to the full hop. That's kind of,
you know, start small and scale up to
it.
The bottom line, architecture co-pilots
are where ROI is going to be won or
lost. And the question isn't whether
you'll adopt one, but whether you'll be
early or late. And if this resonates and
you want to see what an architecture
copilot co-pilot would look like on your
stack, reach out and we'll walk you
through how to best pursue this from our
lens and be able to impart how you can
do it on your own or working with us at
K.io. You can visit kio.te tech to
connect with us or reach us out and go
to gtmio.te
and ask how your team can adopt an
architecture profile for your we'd love
to be a part of your journey.
>> Absolutely.
>> Thanks everyone for joining us today uh
for this session. It was hopefully
informative for you and we are uh we're
delighted that you've given us a chance
to to to tell you more about this and we
look forward to working with you
shortly.