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.