You've used ChatGPT to draft emails, summarize reports, and brainstorm ideas.
But is your business ready for what's coming next?
How do you move from artificial intelligence as an assistant
to a process participant?
Innovators are already experimenting with agents that collaborate, make
decisions, and automate entire workflows.
So where do we really stand today?
My name is Alexandre Nevski, and this is Innovation Tales.
Navigating Change one story at a time.
We share insights from leaders tackling the challenges of today's digital world.
Welcome to Innovation Tales, the podcast exploring the human
side of digital transformation.
Artificial intelligence is transforming business operations, but moving
from simple chat assistance to fully integrated agents is no small leap.
Today we're joined by Konrad Jeleń, a leader at the forefront of this shift.
He serves as Executive CTO at PMR, VP of Data Science and AI
at Kolomolo, and Director for Technical Solutions at AI4Process.
With over 20 years in AI, machine learning, and enterprise transformation,
Konrad has built solutions across industries, from predictive
maintenance in manufacturing to agentic systems in customer service.
He understands the balance between innovation and real world
constraints, ensuring technology delivers measurable value, not hype.
In this episode, we cut through the noise and explore how agents can
collaborate, make decisions, and take on real business processes.
How do you balance automation with control?
When does a system become too costly to justify?
And what safeguards are essential to prevent artificial intelligence
from making costly mistakes?
Konrad's insights will help you navigate these complex questions with
a pragmatic business first mindset.
Without further ado, here's my conversation with Konrad Jeleń.
Konrad, welcome to the show.
Thank you, Alex.
Thanks for having me.
It's great to have you.
I'm so excited about this one.
We both specialize in distilling technical concepts for a business audience.
And you and I, we agreed that we'll be talking about AI agents.
And specifically the fundamental choices companies face when
integrating agents into their systems.
Let's start with some definitions.
So how do you explain AI agents when speaking with clients?
Well, I typically try to get them to feel like they have a quorum of people,
typically experts in some fields, that would be talking about the subject matter
and trying to find a solution to the problem that was given to them to solve.
Okay.
So it sounds like you're using the metaphor of humans working together.
And I guess you've also mentioned something that, to
me, resembles like a workflow.
Are these the necessary and required conditions, or is there something
else that might be a bit harder to explain, to a business person, but
that is still necessary for us to have a system that is considered agentic?
I would say that those conditions are necessary and sufficient to define an
agentic system, with a couple of caveats.
So one is that you would have different flavors of agents, about which I
can tell you a little bit more.
But typically the simplest, agentic system would have just a couple of
experts defined by certain characteristics I can talk about, that would ponder
upon a subject matter to arrive at a conclusion that is of interest to you.
So there's, I guess specialization, that's what you're saying, right?
That, that first of all, the agents are defined in a way that make
them essentially the equivalent of a subject matter expert?
Yes.
That's a, that's a very good depiction.
You would have different models, that are specialized for the different tasks.
So you would have a math expert who, is capable of making mathematical reasoning.
You would have a code expert who would, suggest a piece of code to you.
You would have tools expert who is specialized in calling certain
types of tools to achieve a result.
One such tool could be, for instance, a search engine.
Another one calling off some service on your local infrastructure or on
the internet to give you the answer.
And you would have some reasoning expert who's expert in logic and so on.
And I think for our audience, it's a easy trick these days to just use ChatGPT as,
as a reference point that we can be almost certain that everyone's experimented
with, if you haven't make sure you do.
And, in ChatGPT, you could, have one conversation during which you touch
upon all these different subjects.
and then alternatively, preferably, in fact, you would, have several
conversations and you would use each conversation to talk about
one specific speciality, right?
And I think people can experiment on their own with this and they will see that
what we call task performance, increases, when your chats are specialized.
And I guess that's also what you were referring to, if we're building
an automated system where it's not you initiating each single
prompt, but there is a whole process behind it or a quorum, as you said.
And I guess you've also mentioned at some point, that the agent might be able
to take an action in the real world.
So not just answer a question from its own knowledge, from its own training
data, but, might be able to, you, I think mentioned a search engine, or
I don't know, somebody mentioned on the show to book flights or, whatever.
so is that also a necessary condition as far as you're concerned?
No, although I think as time goes now and development of both the tools
around the agentic systems as well as the AIs themselves, they tend to get
into a form where interaction with the real world through a number of tools
gets easier and gets the job done.
And we would typically try to understand the agentic system as something, some,
well, I said before, like forum of people, or in this case agents that can ponder
upon some idea and then give you a result.
But we would be looking at the agentic systems as something
that can act upon the real world.
And pretty much the only way to interact with the real world is through some ways
of, execution of actions and I find this to be more and more a prominent concept
in the agentic systems that essentially defines those agentic systems of nowadays.
Okay, so we've mentioned a number of must haves.
Agents are specialized, have goals, a degree of autonomy, and I guess more
often than not, uh, external tools to use.
Uh, before I forget, as mentioned in the intro, if this idea of agents running
real business processes has you curious or nervous, join me on Wednesday, May
7th for a live interactive webinar.
In two hours, we'll walk through three examples and discuss questions that
are especially important in risk averse environments such as data ownership,
sourcing options and automation pitfalls.
Seats are limited.
The signup link is below.
With that preview out of the way, Konrad, could you walk us through a practical
example where several agents collaborate so we can dissect who does what and why?
Yes, and I think that would be actually quite close to the
heart of some of our listeners.
I was thinking of something quite generic like customer service.
We actually had recently a hackathon, AI hackathon in Stockholm with AWS.
We've actually built an agentic system that looks at the, inquiry made by
a customer chatting, and, it would classify this, intent into one of the,
predefined intents and further engage on an agentic chat that was specifically
attuned to this kind of intent.
Let's say that this inquiry is a request for help or maybe a return of an item.
let's say a computer part of sorts.
It would be taken by the agentic chat and that agentic chat would first
analyze the sentiment of the inquiry.
It would kindly respond to the clients and look what kind of part is it.
It would further route the conversation to another agent that would get some
more information about this this computer part, let's say a motherboard.
That conversation would happen internally within this agentic chat.
So the customer wouldn't be a witness to what the agents
are talking one to another.
So this information gathering agent would look at the information sources,
reviews, perhaps maybe some forums or a database of products to maybe
find out what kind of part that is a component of this computer part may be
faulty or what problem was previously encountered by maybe other customers.
You would also have an agent that would react on the request for return
of the part and would take over that kind of conversation to work out the
details of the return: the shipping address, maybe information about
when would that part be available, if that part is not in stock and so on.
And ideally, you would have a wrap up agent who would wrap this conversation
up, tell the customer what was done, what is going to happen, and perhaps maybe
ask the customer for further feedback.
When we started the conversation, you used a very simple metaphor
for, what agents are: a group of subject matter experts working on the
problem, but that's a metaphor, right?
it's not like the humans, we specialize because we don't really have a choice.
We simply would take too long to learn all of the different professions in
order to be able to handle a complex service or product these days.
But it's not that an AI system has a limited ability to know things.
We were saying earlier by using multiple agents, we improve task performance.
So is that the main reason here?
And, is that the only reason for splitting those out?
We do this so that every agent is specialized within their
domain and within their role.
One that was described by a prompt.
But there's another aspect to it.
Each and every one of those agents, we can specifically
attune to do best in their domain.
So we can actually specialize them, not only by the means of that script, the
prompt, but also by the actual training, which means that we give them pairs
of answers like a prompt or a piece of chat and then the expected answers.
We do this sufficiently so, that this agent, would get trained to
give best answers in their domain.
And if we combine those experts, into this quorum of agents, then they would
perform the best, uh, in their roles and therefore achieve the goal of a chat.
How much training do you need to do to provide these job descriptions, to
provide these sets of, sample data?
And after that, do you still need to give them like a standard operating procedure
to say you go first, you go second, third?
Or in this case, in this example, where did you guys go?
How much procedural instructions did you give them versus how much are they
actually figuring out based on each case?
This is actually a very good question because I think it ponders upon the
very subject of are agentic chats safe?
Would they, upon, certain way of asking them to do something, would they do
something stupid or outright dangerous?
And the answer is, yes, they can.
And that is a very good big concern currently in the enterprise world
where agentic chats are engaged.
So customers who are initially hyped by the concept of agentic
chats doing stuff that employees did in the past, that concerns them.
And it should, naturally.
And therefore you would like to narrow the, the scope of an agent sufficiently
so that they wouldn't do anything stupid.
And there are certain patterns of building those agentic chats that would allow
you to detect certain things before they can blow in your face, so to speak.
Can you give us a couple of examples?
well, uh, I think, uh, One of your prior examples is a good one.
So you had booking of the flights.
In certain circumstances, that agent would either book a very expensive
flight for you and do this before you can actually react or if that was
airlines, it may do this for free.
So in your agentic charts, you would need to put certain safeguards
that would prevent certain actions to be performed by those agents.
And that would be best achieved by this fine tuning that I have
mentioned before, which is one of the options that gives this agent very
particular examples of misbehavior that you would like to correct.
And there's standard operating procedures that you've mentioned that
would give this agent instructions.
Although you would need to strike a balance between, the actual
instructions for the agent to follow, where it would behave as per the,
their role and also, safeguarding that agent against misbehavior.
But, from my perspective and from my experience, the, one of the
best approaches is, in addition to those two that I have mentioned
is to build another agent and you would call them governance agents.
It would detect misbehavior of other agents, so it can act like across
the agents, spotting behavior that would be emergent across the agent.
It wouldn't be necessarily the fault of one single agent, but it
would simply be a collective fault.
And that agent would do corrective actions to prevent from anything harmful to be
done to either customer or the company.
And, in the worst case scenario, it would ask for a human, governance
kind of operator to step in.
So with the example you selected, very often it would be very detailed
standard operating procedures.
You can hardly afford to have a supervisor listening on every call.
And I think in a way, when you were enumerating the types of agents,
the specialists, the subject matter experts, for in this example, I
felt like this was quite procedural.
So to what degree can you simply replicate that approach and remain very prescriptive
and say, you are smart agent because you know everything about motherboards.
However, I will be very prescriptive about how you interact with the other agents.
What are the flaws or the limits of such an approach?
Well, I think it goes down to the cost.
Because designing agents to do exactly what you want costs money.
It is spent on designing the agentic systems to be comprehensive
enough in terms of number of experts to serve a case.
And in the autonomy of those agents to make certain decisions.
The more you limit agents to make certain types of decisions and the more time you
spend on training them , the more costly becomes the development of those agents.
And at some point there's a break even point between the costs and risk.
So I think the companies would need to be aware of where this risk is located.
There is a question of transparency, right?
And I guess when you're building such a, an agentic system, that's maybe a cost
that maybe isn't immediately apparent.
That you're going to spend quite a bit of time designing things so that the
human in the loop can stay in control.
It sounds like, without that, you'd be forced into being too prescriptive
and losing the benefit of having some intelligence in, in the system, which,
is able to react, In a rational or in a constructive way, to situations,
even if, unlike traditional software, no one actually explained that in
this case, you have to do this.
The agent, guesses that it should be doing that based on the
training data that it, it has seen.
It is a, fantastic question, actually, and a fantastic case.
And thank you for bringing this up.
Like with the human operators, when we build a call center for a specific
customer, when you start it from scratch, you would be building your
standard operating procedures and the kind of balance between what the
agent can do and what they cannot do.
You would be building this iteratively.
So you would, reflect on your prior mistakes on good patterns, bad patterns
and then engage on training of the human operators and create new, better,
improved standard operating procedures.
With the agentic systems, it's even slightly better because you have
all of the conversations potentially recorded in a form of chats and you
can have them flagged, by let's say the governance agents or in other
way, so that you may find out about a correspondence between the outcome of
that conversation, business outcome, and how did the chat go and how far did
the autonomy of the agentic system went?
And this means that you are capable of capturing the key metrics and capturing
the key aspects of the chat and then using it for retraining of your agents.
And you can do this continuously.
Ideally, you would be able to use every one of your interactions
for training purposes.
You would do the interaction with the customer and then afterwards you'd spend
some time analyzing what went right, what went wrong, discussing it with
your supervisor and so on and so forth.
Of course, in reality, there is no time for that.
But with, AI systems, I guess you could potentially, you have
the data as you're pointing out.
So you could use every single case to get better, but you could also have a
governance agent looking every over, over every single conversation, but then you
could also have a governance agent for the governance agent, and you could just
continually pour computational resources until you get the perfect answers.
But obviously that's not realistic.
What's your experience?
Where's the inflection point?
I think it is in the cost and in the business case.
This is one of the things that are important to me, as both an architect and
data scientist, and also business owner.
You would need to find out what the metric is for your human based system
that is important to you and the metric that you would like to achieve.
You also need to measure your risk and that would need to be in dollars
or euro or whatever currency you use.
And then you would need to track that metric and if that development and running
costs are exceeding that of a business case, then this is when the stop is.
I just want to emphasize because you've mentioned costs twice.
You've mentioned a bit earlier when we were talking about the cost to design
a system, to train, to provide the training data, to specialize, to create
the prompts, et cetera, but now you've also mentioned the cost of running.
And I'm not sure that, when you're using ChatGPT, you're really aware of the cost
of running, if you have a subscription.
You are, if you're using the API, which you do, but I think it's, something
that might be useful to explain, how the cost function works operationally.
So you pay for the infrastructure and then you have the AI itself and you can
use either your own, self hosted, or you can ask other company to host it, or you
may use services like bedrock, on AWS, which we actually use and we recommend.
and, you have certain cost per token that comes with it.
And you have to pay for this, and you have to calculate this, and those compounds
together into the cost structure.
And then when you develop your agentic systems, you may develop
your agentic chats with five agents.
You may design it, with 10 agents, 20, 50.
And imagine this: in many cases you would be asking each and every one of
those agents to give an answer or at least a good portion of them, which
means when your message from the customer is comprised out of, an inquiry that
is a hundred or maybe 200 tokens, by the end of the day, it is consumed a
hundred tokens times the number of agents that take part in the conversation,
plus all the answers that the agents give internally that you never see.
and that is a cost.
And those are the running costs that you would need to have.
In addition to this, from time to time, you would need to do some upkeep of
your infrastructure, of your prompts.
And someone has to do this.
Someone has to retrain that AI.
Those are running costs.
Okay, great.
That's I think a very good summary of the costs.
And with that in mind, what's the worst possible system to employ, agentic AI on?
So Like running your, I don't know, entire finance department.
Is that a terrible, a terrible, use case for an agentic system at the moment?
Yes, I think you would end up better if you were to take it in small steps.
Build smaller agentic systems, more specific, more fine tuned, and perhaps
using cheaper models where you can get more bang for the buck, so to say.
There are currently what is called small language models, that are
on the rise, that you can get fine tuned to do a great job.
There are techniques called model destillation that you can use to get,
domain knowledge of a larger model into a smaller model that is ten times or
even a hundred times cheaper to run.
And those are the techniques that you can use.
But those are very domain specific, which forces you to think in agentic domains.
Like you have agentic chat for returns, going back to our
example for customer service.
Agentic chat for giving you more information.
You have agentic system for dealing with complaints.
You have agentic system that deals with upgrades off your hardware.
It can give you the best possible setup for what you want.
And thinking in those terms is probably breaking the promise of, the big AI
systems, omnipotent, but at the same time, it is closer to reality, more practical.
And I think, our job as AI kind of people, is to give those companies, that were long
waiting for AI systems to be implemented for them to save them some money, to give
them practical solutions that really save the money and give them better results.
And as we're about to wrap up, I usually ask a couple of questions the end.
The first one, is there a book, a tool or habit that has made a particular
impact on you in the last 12 months?
I have read so many books, but one that struck me the most is a book written
by Stephen Wolfram, and he wrote a book that I recommend for everyone who's not
very technical about AI and would like to learn a little bit more about it.
There's a bit of science in this book.
And that book is How ChatGPT Works, if I got the title right.
We'll make sure that the link is in the description.
Absolutely.
It's a fantastic book and, Stephen Wolfram is a fantastic scientist.
And he can talk about things that are complicated in a way that
is understandable even to me.
Perfect.
And finally, what's one thing that you expect to remain
constant 10 years from now?
The AI systems are going to get better.
and, we would see advances in the AI computer science and in the mathematics.
And, of course, end result is going to be that those chats are going to be better.
But I think for business people, always looking for metrics and making sure that
your AI solution is behaving within the metrics that you put it to behave against.
That is one, one thing that I would always recommend to everybody,
regardless of how smart the agentic or AI system is going to be.
Look for metrics.
Don't make the mistake off going after the hype and just harming
yourself and your customers alike.
great point to finish on.
With that, Konrad, a true pleasure!
A pleasure is mine.
Thank you for having me.
AI agents are evolving from research assistants to active process participants.
But as Konrad highlighted, success isn't just about automation,
it's about balancing autonomy, cost, and accountability.
Specialized agents can enhance task performance, but without
proper governance, AI can introduce risks instead of efficiencies.
So as you think about AI in your own organization, ask yourself,
where is the right balance between automation and control?
And how do you ensure AI is adding real value, not just complexity?
If you want to continue the conversation, connect with Konrad on LinkedIn.
The link is in the description.
As always, we have more exciting topics and guest appearances lined up, so
stay tuned for more tales of innovation that inspire, challenge, and transform.
Until next time, peace.