Google Cloud Speech-to-Text API (Chirp) + Gemini 2.5 Flash Non-Thinking. Treat it as a searchable index of what was broadcast, not a quotation record.
00:00:00The most important origin, because
00:00:03with us, the main roles are only given to originals from Croatian
00:00:07fields. Domestic in the main role
00:00:10Podravka
00:00:13broken.
00:00:35sebe u firmi, onda sad upravljaju u
00:00:41computers, to printers, to
00:01:05Good day and welcome to another AI central point broadcast.
00:01:08We continue with a new series of episodes in the
00:01:11second half of the year. Today's topic will be AI
00:01:14agents, so when software stops waiting for
00:01:17commands. With me in the studio are my guests today,
00:01:21Nenad Raca, CIO of Aduro Idea, and Krunoslav
00:01:24Kedmenec, program manager at
00:01:32Until yesterday, or even today, we asked artificial intelligence something and it gave
00:01:35answers. Mm, this year we are starting to give it
00:01:39tasks. Are you ready to let software
00:01:42actually do something on your behalf, maybe even without asking you
00:01:46beforehand? Yes, we are, it's already
00:01:49safe enough, ready enough. Okay, great, good, we'll get back
00:01:53to that. Yes, we are doing that currently. You are already doing it, that will be very
00:01:57interesting, I think, for many listeners to hear. They are thinking about it.
00:02:00So, we already have people in Croatia who, without problems, with full confidence,
00:02:04mm, use agents. I actually wrote some
00:02:08articles about agents two years ago and I predicted that 2004,
00:02:1124 and 25 would be the year of agents. I miscalculated a bit,
00:02:15it seems it's only 2026.
00:02:18And today everyone talks about agents, uh, but what are
00:02:21agents actually, what is it actually and how would Nenad describe the difference between
00:02:25a chatbot, automation and actually a true AI agent? In fact, that's
00:02:29the most... important difference. A chatbot answers questions,
00:02:32automation always does the same routine work and there are no
00:02:36changes, and an agent, in
00:02:38quotation marks, thinks, makes decisions, knows when it can do
00:02:42something and when that task should be handed over to a human. So, it has a certain
00:02:46level of responsibility and decision-making in all of this. Hmm, is
00:02:49an agent actually a new technology or is it
00:02:53a new level of trust we have in old, already developed technology?
00:02:56How much has that change happened? That is actually the most important part, that new level of trust. The
00:03:00technology is not that new, all those parts that we are incorporating
00:03:04have existed for several years, but now we have shaped them so that we can
00:03:07really trust them, or rather that mechanisms of
00:03:10trust and verification of all those agents are established. In
00:03:13fact, with agents, we are opening a new dimension where
00:03:17these things actually become our new digital employees. Hmm, hmm. Well,
00:03:20yes, an agent should be seen as another interface between
00:03:23human and computer. Hmm, so
00:03:27you no longer have a keyboard, or if we go to a higher level... you no longer
00:03:30have, you write a specification, then someone who
00:03:33translates it into computer language, no, you now directly communicate with
00:03:37the computer and directly give it a task. So, that's your
00:03:40intern. How did that process actually go at Verso Altima
00:03:44and did that moment happen where you said, okay, this is no longer just
00:03:48a tool, this is already an agent with a certain level? Did that
00:03:52happen, was it a process that took time? Well, in principle,
00:03:55we started last year, each of us individually, using
00:03:58AI for something. What it does for some documents, for
00:04:02some, uh, review of some contracts and so on, but those
00:04:06were islands, it was exclusively communication
00:04:09uh with this LLM,
00:04:13then the analysis of what needs to be uh for us
00:04:16Aduro, when we went to try if we could
00:04:20use AI for
00:04:23cyber, they were the first to come with an agent and it was like wow,
00:04:26it works, that was the wow moment. That was even before
00:04:30Anthropic released it commercially and the moment Anthropic
00:04:33released the personal agent, that was
00:04:37it. So, there is that wow moment when you saw something and despite the experience
00:04:40you said, okay, this is much better than what we expected.
00:04:44- I think it's good to mention Kruno, you actually have an example where agents
00:04:48within the company have already done the work from beginning to
00:04:52end, meaning redesigning some corporate websites. But can you
00:04:55walk us through what the agent did, what the human did? That's
00:04:58a great example where almost anyone can
00:05:01do it. Mm, agents really like
00:05:04uh examples. Hmm, this is excellent. You take
00:05:08an agent and say, these are our corporate websites, this is how I would like them to
00:05:11look. Great,
00:05:14then the interaction begins, which is, let's say, more or less something like chat
00:05:17ideas are elaborated and the agent makes
00:05:20a plan on how we will implement it. Okay, that is given
00:05:24to another agent, who then codes it. Then it goes to
00:05:27transferring that code to WordPress. And that's where it got very interesting, because
00:05:31my colleague gave the agent the task of transferring it to WordPress, but it wasn't quite right. Then he
00:05:34kept reporting, no, this is not good, change this, then he was
00:05:38frustrated and gave the agent the task of
00:05:40comparing what was done and what was on WordPress, and the agent
00:05:44reported back to the other agent what needed to be done, and today we have beautiful
00:05:48websites. So, here's a question that will interest everyone in
00:05:51business. This might also be a good time to mention that we are organizing
00:05:54an artificial intelligence conference in Rijeka for entrepreneurs. As an entrepreneur, I would
00:05:58like to know how long that process took with agents, and how long it
00:06:01would have taken without agents, if only people had done it. What is the time saving?
00:06:06Well, even more than 10 times, I would say that
00:06:09uh days and weeks are... hours.
00:06:13Aha, so what you used to need several weeks to do,
00:06:16have meetings, talk to people who would
00:06:19create graphics for you, you would make
00:06:21icons, check all the texts, that literally took
00:06:25a few days, maybe. Maybe the biggest benefit is not just time, but
00:06:29the fact that we suddenly have the capacity to, instead of working, talk a lot
00:06:33and decide what to do, we make those five versions, look at which one is
00:06:36the best, and practically immediately choose from choices that were made very quickly
00:06:40and efficiently, which are complete, which are not conceptually, and yes, they are not prototypes but
00:06:43practically functional, in this case, websites. So, I would
00:06:47say I think many members of UGP will be more interested in hearing
00:06:51about this. Nenad, let's go back to Aduro Idea. So, you have experience and have been
00:06:55working with agents for some time and actually with agent
00:06:58operational centers. Uh, what is that exactly, how to
00:07:01explain it to someone who is not from IT for starters, and again, let's go back to
00:07:05some of your examples, 10 times is a huge saving, so practically 90%
00:07:09of time is saved. Does that match your experiences? Well, my experience... actually a little
00:07:12less, but because of this playing part, that everyone suddenly has the opportunity to play,
00:07:16or rather, I don't know, sometimes when we were developing software we always talked a lot at the beginning
00:07:20before we started working. And now we allow
00:07:23ourselves to start working on different versions and see which one
00:07:26will be the best. So we have a little more wasted time, but the improvement is still
00:07:30significant in that whole story. What you
00:07:33mentioned about what an agent system is,
00:07:37it's probably best if we consider agents as digital employees.
00:07:40Hmm, the entire operating system for...
00:07:42is actually their building where they work, with their rules,
00:07:46like every company has its own additional company for
00:07:49digital people, for digital agents who work for it, in
00:07:52which physical people can enter, can monitor it, can see how it
00:07:56functions, how it functions, create new agents within it.
00:08:00Hmm, that's perhaps a new thing now. Until now, we all looked at who has which
00:08:03employees, how many employees, how they are organized. Now we will
00:08:07have a new sector that will look at how many employees there are,
00:08:11what they are like, how they are organized, how they work, how efficient they are, how
00:08:14accurate they are. Yes, I mean, that part sounds fantastic, although it still seems a little
00:08:18abstract to a very large number of people in Croatia, so we will definitely get back
00:08:22to that, but here's a question for you, and perhaps for Kruno.
00:08:25So, where in an average Croatian company is an agent
00:08:29used, or where can it be used to be actually
00:08:32most effective, and where are these not some spectacular applications? Well,
00:08:35I mean, there are many of these trivial ones that everyone pushes first, like customer
00:08:39service. Those are things that are very easy for everyone to understand.
00:08:42Easy to understand how it works. But maybe there's that part that all
00:08:46people forget, that they use very often, and that's the part of being a kind of consultant in
00:08:50certain decisions, where we start inserting some
00:08:53documents, communicating, and actually creating
00:08:56a new consultant with whom we can discuss some
00:08:59topics where sometimes people in the company don't have the right interlocutors, so they used to
00:09:03look for them outside. Now, with an agent, they can have an interlocutor with whom they can do that,
00:09:07analyze documents, analyze decisions, try out some
00:09:10scenarios or any such things. A concrete example, you have a large
00:09:14project with many subcontractors, main
00:09:18contracts, contracts with subcontractors.
00:09:21Give the agent the task to read all those contracts and to
00:09:24say, these you have covered, these you have not
00:09:27transferred. So, no, that's not, that's still not
00:09:30a lawyer, but it immediately provides insight, especially agents are
00:09:34very good when it comes to putting together information from
00:09:36different sources and doing it very well. We had an episode about artificial intelligence
00:09:40in law some time ago, and people who
00:09:44work on specialized systems for artificial intelligence in law testified to that. So, we can always
00:09:48refer to that episode. Some
00:09:51people complain again, so I have to ask you, are we at a stage where agents save
00:09:55us as much time as you said, or do they create new work for us in the sense that
00:09:59someone still has to supervise these agents? Uh,
00:10:02I don't think that supervision is a problem, because it's basically
00:10:06like, uh, how would I say, when we had computers, when they arrived, we all
00:10:10became secretaries, and we all
00:10:13started doing that administrative work, and in fact, the jobs of secretaries didn't disappear, but
00:10:17we all became our own secretaries. Hmm, what we are
00:10:20becoming now, we are all actually becoming managers, because we all now get our own
00:10:24digital teams that we start managing. So,
00:10:27when you look at it, it's not taking away jobs, but it's actually defining
00:10:31some new roles for people who perhaps were not ready for them until now. Yes, no, the question is
00:10:35whether new work is being created, because you now have to control agents that
00:10:39you didn't have to control before, but my experience is that the scope of work
00:10:43has not increased, that we are on the winning side overall. The type of work has
00:10:46changed, because we are doing more work, rather than controlling someone
00:10:50or some people who are doing work for us. And people who have
00:10:54managed infrastructure until now, first they managed infrastructure
00:10:56within their company, then now they manage it
00:11:00in the cloud. Those same people will manage the agent infrastructure, they will
00:11:03make sure everything works, that not too many tokens are used, that it
00:11:07functions. But we can imagine, building on what Nenad
00:11:11said, an AI agent as a board advisor, for example, meaning
00:11:15someone who will help in decision-making. I think that's a very interesting part and I agree
00:11:18it's often neglected but potentially very good. But
00:11:21okay, mm, I'd like to ask Kruno one more thing. "Actually,
00:11:25you have a very interesting project, namely, AI for Cyber, which is
00:11:28a research and development project for specific cybersecurity,
00:11:31a topic very dear to me, which is why we had an episode
00:11:35specifically dedicated to it. So, it's being carried out with funding
00:11:38from the European Union, you managed to get that. What problem did you want to solve with agents
00:11:42with this? The problem is, one of the things Verso Altima
00:11:45does is cybersecurity, mhm, not
00:11:49only for IT systems but also for smart
00:11:53city management systems."
00:11:55and industrial control systems and so on. And there you
00:11:58have today's software, today's methodology is
00:12:02pattern recognition, hmm. You constantly
00:12:04maintain a
00:12:07database of event patterns, it
00:12:11recognizes them and gives them to an expert for assessment
00:12:14whether to react. Unfortunately, there are a lot
00:12:18of false alarms, a lot of things
00:12:20that recognize a pattern, but we know it's just some
00:12:24test. Hmm, what we, as I said, what was that
00:12:28wow moment, we gave a hastily put together
00:12:31agent to just go through a bunch of logs and it started
00:12:35spitting out things we hadn't even thought about.
00:12:39So, we are moving from pattern recognition to
00:12:41anomaly recognition, hmm. Is something
00:12:45happening that usually doesn't happen? Am I logging in
00:12:48at 2 AM and mistyping my password 10 times? Hmm,
00:12:52hmm. I have to ask right away, it reminds me of some examples from other
00:12:56industries. How much time was actually wasted until now on
00:12:59sifting through those false alarms,
00:13:03and actually how much has an agent accelerated that and
00:13:06saved time? Here, the approach changes, right?
00:13:10The previous approach was to search for something, but here
00:13:13the agent searches for anomalies in everything that behaves, and
00:13:17the most important thing is, perhaps when people looked at it, the problem was always that we were looking at
00:13:21certain different sources. Here, with agents, it's already possible to look at
00:13:25connections between totally different sources that occur at a certain time and that might have
00:13:29some causal links. So, when something is hidden or
00:13:32disguised within some other events, I assume it's more
00:13:35efficient. Yes, for example, another big project we are working on is
00:13:39the use of agents for monitoring huge telecom networks.
00:13:43Aha, you have very trivial situations where somewhere in
00:13:47Split there is a device that has an old version of
00:13:51Finware. And how do you find that? So, you are looking for what Ned said, you are looking in
00:13:55what no, you let the agent, tell me what's wrong,
00:13:58and he, he nicely says in Split there is a
00:14:01device that has an old one and do you want me to
00:14:04upgrade it? Yes, that's what you'll let
00:14:08the agent do. Today he asks, today he asks you. I think that
00:14:12example will interest many companies, like where we all have to upgrade companies, no,
00:14:15absolutely. Super, Nenad, but what
00:14:18happens, let's go back to that topic. If attackers use
00:14:22agents for some cyber security mm... problems, does
00:14:25the speed of attacks actually change, how important is human
00:14:29defense there, does a human even have time to react? It's not even a question of how
00:14:33fast it is, but how inventive it becomes, hmm.
00:14:36Right, that's a new level, because until now that
00:14:39part of invention was done by special people who had the gift of finding such
00:14:43things, and now with agents, or rather
00:14:47swarms of agents, that means now we are no longer talking about one agent, we are talking
00:14:50about them, which can be hundreds, acting simultaneously, communicating
00:14:54with each other. They can carry out very simultaneous and diverse
00:14:57attacks and diverse testing of different things. So,
00:15:01uh, we are currently in the company using and testing
00:15:04uncensored models to test the security of our
00:15:07applications. So, instead of doing the traditional testing of
00:15:11applications, whether the code is secure enough and everything, we simply let an agent
00:15:15who has permission to attack the application, to try
00:15:18what it can do with the application, because simply new patterns will emerge and
00:15:22new patterns are emerging, new things that work. Yes.
00:15:25Innovativeness is always a specially interesting question. It's interesting in this
00:15:28context to say that just these days, information came out from OpenAI that
00:15:31with the newest model, they managed to solve one of the extremely
00:15:35difficult mathematical problems. In that way,
00:15:38again, suggesting a completely new kind of innovativeness and knowledge, so
00:15:42let's hope it won't reflect in all these other security
00:15:46problems, I think it will, because
00:15:49a colleague, one of my physicist colleagues who uses
00:15:53AI in that whole story, he said just recently that
00:15:56he was getting such solutions, meaning he uses
00:16:00AI very intensively in his work, meaning that he gets solutions where he
00:16:03sometimes has to think hard about the approach that AI took.
00:16:07Which, in the end, very often turns out to be very good, but at first it wasn't
00:16:11visible to him at all. That's the thing, how AI helps us
00:16:14find inventive approaches to something, anomalies. In fact, let's
00:16:18stay on that risk for a second, meaning today, still a small
00:16:21number of companies use agents, but I'm sure that will increase, especially after they hear all of you.
00:16:25An increasing number. But then we ask ourselves, what is that risk, because if we
00:16:29use some agents, we have given them access to our data, to some decision-making,
00:16:33how risky does that actually become for us, is that something companies should
00:16:37be afraid of? I mean, they should be afraid of it just like
00:16:40anything else. Uncontrolled, unorganized access brings
00:16:44problems. If it's approached smartly,
00:16:47organized, so that what is being done and what is being managed is known, what
00:16:50rights are given, because it's not a problem if the agent has the right to what
00:16:54it needs to do, but if it doesn't have the right to what it needs
00:16:58to do, it won't do the job. If it has too many rights, it might do
00:17:02too much. So, it's the same as people, you need to look at its
00:17:05experience, gain experience in... managing, then accordingly give
00:17:09rights, manage rights. Fire is a good servant, a bad
00:17:12master. The same applies to agents.
00:17:16If, as Nenad says, you know very well what you are doing, how you are doing it,
00:17:19what rights you are assigning, there are no problems. If you just let it go,
00:17:23you've thrown a match on dry grass and everything's gone. Okay, but who's
00:17:27to blame, for example, if an agent deletes the wrong email or
00:17:30sends the wrong offer? Always the organization and those who have
00:17:34defined the processes. The software cannot be blamed. Hmm.
00:17:37It does what it's allowed to do. So, no agent will ever do
00:17:41anything outside of what it's allowed to do and what's defined as its
00:17:44task, but it will try with all its capabilities to complete that
00:17:48task. These incidents that are talked about a lot, it was given the task
00:17:51to do that and they watched how it would do it. It surprised them how
00:17:55it did it. Yes, that's this speed, that is, how quickly
00:17:58it can happen. That's this inventiveness and speed with which it can be
00:18:02created. Yes, I think that's a special question that we probably
00:18:05won't go into today, but for example, when we have... many agents on both sides, and
00:18:09so maybe it's not a bad context for my next question. So, we've been researching
00:18:13"human in the loop", hopefully our article on that topic will be out soon. When is
00:18:17a human in the loop important, when is it necessary, and when is it actually
00:18:20unnecessary or impossible for them to do? Because as humans who
00:18:23approve something, we can look at 10 things or maybe 100 things or a few hundred, but
00:18:27if it's 10,000, 100,000, a million cases that we have to,
00:18:31the human brain simply cannot process that. Mm, what are
00:18:34your thoughts on this? Well, we need a human in the loop. That is, a human
00:18:38in the loop, meaning where the human is, I mean, simply so that we don't have them just for formality, that's
00:18:42important, not for formality, but where they are truly needed. So, truly in
00:18:45tasks where the crucial thing is that something happens,
00:18:49that something is done, but there must always be some
00:18:51mechanism for it to be done, because there are many
00:18:55cases where a human will be put in the loop just
00:18:58to perhaps avoid some legal responsibility, and then he'll be blamed,
00:19:02he didn't look, but if he clicks 10 times he will click 11 and
00:19:05111 and 1001.
00:19:08Automated, it will become automated, we have actually made a robot out of a human. Hmm, what
00:19:12I say, just last week I was talking to
00:19:14colleagues, I realized where the bottleneck is
00:19:17in using an e-agent. I, yes, I can no
00:19:21longer keep up with how quickly it can analyze,
00:19:24suggest, ask a multitude of questions from
00:19:28different aspects, I physically can no longer
00:19:31keep up with it, so not just approval, but also the work with
00:19:35the agent itself is too fast for us. I've heard that from... several sides, exactly,
00:19:38exactly that experience. Then the question arises, so on the one hand
00:19:42we have the AI Act which requires human supervision, and
00:19:45then we ask ourselves when that is, is it feasible in practice or is it just some words on paper where they told
00:19:49me that a human is responsible on paper but actually
00:19:53doesn't have the ability to do it because it's a bottleneck? Yes, but again, for that, we need to know how it works,
00:19:57meaning that's again, we're back to the standard thing, enough testing, where again
00:20:01e-agents can help, because we can also use agents to test agents.
00:20:04So, what we said, if they can carry out some
00:20:07attacks, they can do anything else. An AI agent can test an AI agent and
00:20:11find ways, or rather, problems in behavior at points
00:20:14where human supervision is truly
00:20:18justified. Yes, and you teach him while you work with him. So, all this that
00:20:21I'm saying, I can't keep up with it, but every iteration, he understands better and better.
00:20:25In the end, I no longer need him, that's that
00:20:29moment where I let him do what I assigned
00:20:32him, but by then we have solved it. My experience is that it takes
00:20:36about a week.
00:20:39Hmm, yes, I think that's the key part, that agents actually learn from us, and my
00:20:42experience is, lately I've been playing with an AI second brain, so that's
00:20:46a system that is being developed, which has several agents connected, and it's constantly learning from me.
00:20:50It collects data, so I assume that will also be a part that I
00:20:53plan to, let's say, deal with educationally, meaning
00:20:57helping people create all their second brains that will help them, that will learn from
00:21:00them and actually help them be 10 times more efficient or even 100 times
00:21:04more efficient. But here we need to take into account, what you mentioned, these are personal
00:21:08agents, right? A second brain and such things serve me personally. If we go to
00:21:12a corporate level for company use, then that
00:21:15approach no longer applies, because then uh, we can't let someone, in quotes, for... memory, that memory must be
00:21:19controlled. So, access to memory
00:21:23in the corporate world and in the private world is totally different, and that's the
00:21:26thing that I hope people won't make the mistake of trying to
00:21:30use private agents. And what works well privately in corporate
00:21:34is good, so separating my second brain as an agent, as my private
00:21:37multiplier of my efficiency, which is a fantastic thing, and
00:21:41corporate agents who are made and controlled differently and so on, great, that's
00:21:45an important thing for. Yes, that's my experience, I have to
00:21:48separate private matters. Hmm, so if I'm looking for LED lighting
00:21:52for an aquarium, that shouldn't be within the AI for
00:21:55Cyber project. Those are two different contexts, because at the beginning, before I
00:21:59mastered those skills, then I was getting strange answers, and then I realized,
00:22:02well, yes, I asked it some nonsense within the same context,
00:22:06now it concludes that I need an aquarium for cyber. Great,
00:22:10great, let's then give one more, maybe, practical advice
00:22:13to the viewers. A company comes and says, "I want an agent," we're interested,
00:22:17that sounds great. What should they have first, how? To start that process,
00:22:21maybe it has nothing to do with artificial intelligence? Well, first they need to have
00:22:24a clear idea of what they want to achieve. Okay, second, they need to make
00:22:27a decision that they want to change how they want to work. Because
00:22:30implementing AI in a company without changing the way
00:22:34the company works is doomed to failure. Hmm, so those are all
00:22:37projects that fail, they fail because the basic work process has not
00:22:41changed. The basic work process must change because AI is not an addition to
00:22:45the process, it is an evolution of the process itself, which starts working immediately after that.
00:22:49Then we can go... to technology and everything else. That's perhaps
00:22:52the most important message. So, my background is it, al'
00:22:55if it doesn't fall into it, it's clear, have you
00:22:58managed to change the processes and mindset within the film, my advice is to get
00:23:02a personal AI agent who will guide you through this process, I know
00:23:05some people who have done it and are very satisfied with it, I must
00:23:09say even at the level of personal development, what insights they have gained
00:23:12into their functioning and behavior, how much does it cost to get into
00:23:16such projects at all, how much does it cost to get in if you do it smartly?
00:23:20How much does it cost if you do it the wrong way? Let's start with this, if you do it the
00:23:23wrong way, there are those penalties for GDPR European Ai act,
00:23:27that's how much it costs to do it the wrong way, so that's the ultimate
00:23:31price that is paid for all that plus lost time plus lost
00:23:34reputation plus everything else to get in smartly
00:23:38we can already do it for a few thousand euros, mhm, so these are not
00:23:41some abnormally expensive projects, abnormally expensive projects, so the important thing there
00:23:45is that these are no longer projects, so it's not something that is created from
00:23:48scratch, there are ready-made things that exist, that work, that can be
00:23:52tried out, because uh, I don't know, in our approach we always
00:23:55give people access so you can try it out, you can
00:23:59get a demo, you can see how it works before you decide
00:24:02to implement it, we don't have to enter risky projects, let's enter
00:24:05something small, a small process, but we provide support
00:24:09on how to redesign such things, not just let's
00:24:12force it into the process and everyone who does something will have their own
00:24:16AI assistant to ask something, because realistically, then they haven't sped up the work. "I hope
00:24:20you smartly entered that project, uh, of course, first
00:24:24start with something you know well, mhm, mhm, so
00:24:28you can assess whether what the agent is writing to you
00:24:31is good or not, yes, I often play dumb when
00:24:35talking to him, I deliberately won't tell him, I want to test how he understands
00:24:39and I want to see what all I have to tell him, explain to him, what
00:24:42documentation I have to give him so that he can give me a quality answer so that
00:24:46he can assign work, so start with something I know."
00:24:50That's why I said websites, nothing
00:24:52specific, issuing invoices, checking if
00:24:56there are any unpaid invoices in the entire database of excel
00:25:00documents, financial analyses, some reports automatically, so there are a whole
00:25:04bunch of great things, we will continue to explore this, but for example, you
00:25:07are definitely companies you work with and clients are some early
00:25:11adopters, no, we always have those who need a little more who are skeptical
00:25:14about what is happening, what is developing, what would you say to a director
00:25:18who says: "Let's wait another year for
00:25:22it to settle down, and then let's see and maybe introduce an agent, uh,
00:25:25the biggest, biggest problem you can create in business
00:25:28is not following what is happening, that is, waiting
00:25:32too long mhm and not trying things out, so no one says
00:25:36that companies absolutely have to implement this in their business at this moment, mhm, but
00:25:40they have to start using it, but not in a way where we ask chat and
00:25:44chat answers something, but let's try to test some
00:25:47processes, see, we have the opportunity to... learn, technology is
00:25:51new, there is still no one who can say with this
00:25:54technology this, this, this can certainly be done, and this certainly cannot
00:25:57be done, we are all experimenting here, so it must be tested, we are all testing, we are all trying, so
00:26:01all the time delaying. is a waste of time for
00:26:04learning, those who start in half a year, have already lost half a year
00:26:08of learning, those who start in a year have lost a year of learning,
00:26:11because changing the company, what I mentioned, to change the way
00:26:15the company functions is a prerequisite for
00:26:17implementing AI, that is, AI agents, that is, digital
00:26:21employees, opening new departments, new things in the company itself, how it will
00:26:25work digitally, so these are no longer digital software, these are
00:26:28digital employees, so he provides complete work and results and
00:26:32control and supervision over him, super similar processes as
00:26:35it was, digitalization of offices, when we
00:26:39switched from paper to
00:26:43networks, so to make a transformation that, you know,
00:26:47you have to see what the possibilities are, that is, learn what
00:26:50you can do with agents then make decisions and say great, an agent would greatly
00:26:54help us here and start there, oh great, I can
00:26:58only say how lucky we are who deal with education in artificial intelligence,
00:27:02mm... guys, thank you very much, we will continue this conversation in AI Second Point which will
00:27:05be broadcast on YouTube and with this I will
00:27:08conclude this show about agents, they are an extremely interesting
00:27:12topic, we will certainly return to it, and we plan to cover
00:27:16other interesting topics such as artificial intelligence in marketing or artificial
00:27:19intelligence in the defense industry, artificial
00:27:22intelligence perhaps even in religion or journalism, but
00:27:25you will learn all about that in due time. Thank you all again for your
00:27:29attention until the next AI Central Point show.
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00:30:50Sea, the Russian-Ukrainian war against food,
00:30:54Nepali guest workers, what Sherpas do in
00:30:57Norway.