← The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#686: Autonomous innovation using predictable AI agents with Peter van der Putten, Pega
00:26:36
Loading summary
Transcript48 lines
- [00:01]
Dish Network Announcer
Dish has been connecting communities like yours for the last 45 years, providing the TV you love at a price you can trust. Watch live sports news and the latest movies, plus your favorite streaming apps, all in one place. Switch to Dish today and lock in the lowest price in satellite TV starting at $89.99 a month with our two year price guarantee. Call 888-add-D Dish or visit dish.com today.
- [00:31]
Liberty Mutual Insurance Spokesperson
Support is available 24. 7 with VRBoCare. We're here day or night, ready whenever you need help because a great trip starts with the right support. The agile brand.
- [00:58]
Greg Kilstrom
Welcome to Season seven of the Agile Brand where we discuss the trends and topics marketing leaders need to know. Stay curious, stay agile and join the top enterprise brands and martech platforms as we explore marketing technology, AI, E commerce and whatever's next for the Omnichannel customer experience. Together we'll discover what it takes to create an agile brand built for today and tomorrow and built for customers, employees and continued business growth. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, AI and marketing operations. The Agile Brand Podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com to make sure you always get the latest episodes, please hit subscribe on the app you listen to podcasts on and leave us a rating so others can find us as well. Now onto the show.
- [01:55]
Podcast Host
What if your agentic AI could innovate autonomously and still follow your business rules? Agility in the age of AI doesn't mean just speed. It means predictability, accountability, and the ability to innovate autonomously without businesses losing control of what's important and what their customers value. Today we're here at Pegaworld 2025 at the MGM grand in Las Vegas and we're going to talk about how enterprises are starting to move beyond prompt based, freewheeling AI models and towards something more mature, governed and scalable predictable AI agents. And we're going to explore what that means for the future of autonomous enterprise decisioning and innovation. Tell me Dig into this topic. I'd like to welcome Peter vanderputton, Director AI Lab and Lead Scientist at pega. Peter, welcome to the show.
- [02:45]
Peter Vanderputton
Yeah, thanks for having me.
- [02:46]
Podcast Host
Yeah, yeah, welcome. And welcome back. So, you know, I think it's number three here. So you're. You're going for a record. I love that. So let's for those that didn't catch you before, why don't you give a little background on what you do at pega.
- [03:01]
Peter Vanderputton
Yeah. Awesome. So my formal title is Director of the AI Lab and the lead scientist for pega. So I'm responsible for AI innovation. I report him to our cto, Don Sherman. So I'm pretty much PEGA being a platform for AI and automation. I'm pretty much his AI guy. That's the shorter version of it. Nice. I really look at innovating. Yeah. Helping our clients understand how they can innovate with AI, but we also need to kind of not eat our own dog food, but drink our own champagne. So how can we also, you know, renew Pegas brand from AI point of view, come up with new. Go to markets for AI like generative AI, generic AI, but also more kind of on the, let's say, the technical side or even the research side. What are kind of some of the latest AI innovations outside of pega and bring them to. Well, bring them to pega. Right. So got one foot also in university as an assistant professor. So there I need to keep up with the cool kids, you know, are way smarter than I am. But that's a good way to get that external lens in a way on what's happening in AI. Yeah, love it.
- [04:13]
Podcast Host
Well, yeah, let's dive in here. We're going to talk about this from a few different perspectives. But first, I mentioned the topic predictable AI at the top of the show. I'd love for you to. What do we mean when we talk about that? Think that's something that certainly was already introduced this morning in the keynotes at pegaworld.
- [04:32]
Peter Vanderputton
Yeah, absolutely. So we talk about these, particularly in the context of agentic AI. We talk about predictable AI agents. So to kind of define a little bit what it is, let me just maybe peel the onion a little bit first. Agentic AI. What's the idea there? In general, agentic AI is AI that has more agency. So that can actually. Well, when we look at generative AI so far, the likes of ChatGPT and whatnot, they're amazing what they can do, but they're also quite passive. We just need to give them a prompt and then they will give an answer. And that's about it. When you think about agentic AI, we want to have, let's say, AI systems that can operate in a world where they can sense the environment, they can understand what certain goals are to be achieved, and they can do things like planning, figuring out how they can use different tools and take actions to get to a particular outcome. So that's Kind of flipping around this passive role of genai more into kind of an active type of AI and that ultimately then unlocks, as we call it, the autonomous enterprise now, the predictive AI. Predictable AI agents, the ID theres that we're kind of playing into, into this point that you can't just say, I'll unleash a mob of agents and they will kind of magically.
- [06:02]
Podcast Host
What could go wrong?
- [06:03]
Peter Vanderputton
What could possibly go wrong? Right. So. Yeah, well, what could go wrong is that this stampede of agents is going to do all kinds of irresponsible stuff. So predictable AI agents for us is really kind of addressing this concern that enterprises have around. Yeah, how can we on one side empower these agents so that they have the right tools, et cetera, but also how can we govern them so that they are doing the right things? And how do we do that? There's a mix of approaches. One is that we say when you hear, in the industry, when people talk about agents, they very much talk about agents. Let's say at runtime, you know, like when you apply for a loan or you file an insurance claim, agents can actually help reaching particular goals. But you could also say, why not use agents also at design time, when you're kind of developing these apps, maybe they can take in all kinds of, these agents could take in all kinds of requirements, walkthroughs, demo videos, you name it, and then help generating the right application. But that could be a very predictable application because there are particular workflows in there. Let's say if you. Well, if I take that claims example, that would investigate a claim that would check whether there's a fraud issue or see what would be kind of all network services that we could offer to the customer that's finding the claim, you name it. So we're then also using kind of this agentic ID not just runtime but at design time to figure out what the application should be.
- [07:43]
Podcast Host
Well, and that design component and Pega blueprint, there's, there's, there's a lot to that. I think that that seems to be a, I don't know, a differentiator, so to speak of, you know, there's lots of talk, I think, you know, first it was generative, now it's agentic AI. There's lots of talk about this. But to your point, without guardrails, those agents can, I mean, at best they just do less than optimal things. At worst, maybe. Yeah.
- [08:14]
Peter Vanderputton
And it's both, both in terms of empowering them and keeping them safe. Right. So I think that's that. So let's say I'll be doing a presentation here also at pagaworld and I'm giving these examples of, I don't know, we're underwriting a life insurance policy. Maybe we need, there's some elements of a medical check, the decision whether a medical check needs to be done. That's not something you want to leave to the agent. That's something where you have governed business rules that you want to execute. If then the decision is that medical view needs to happen, that's then a process that gets executed because on some governance steps that need to happen or maybe some people need to come into the loop to actually give the final call. So it's really about then also blending, giving the agents the right tools that are entitled to use, but also the very predictable tools, the business rules, the more traditional AI workflows, you name it, connecting them to those tools.
- [09:24]
Podcast Host
And it's also about using different types of agents. Right. So you mentioned design, but design conversation agents, automation agents and so on. I know you described it a little bit, but can you talk about how do these kind of work together?
- [09:40]
Peter Vanderputton
Yeah, let's talk through use case. Let's say we have this, I'll stick with this, let's file an insurance claim because that's the most exciting problem of course in the world. Maybe, maybe not, I don't know. But of course we can use these design agents to actually, let's say there's some legacy application from 40 years ago and God knows no one knows actually what it is and how it works. But we do have some training videos available that we could use. There's some old documents from ages ago. So we can then use these agents to actually figure out like based on all that kind of rudimentary information, maybe some information we got out of our process mining as well. Like what's really happening when we look across history of the last 10,000 claims and use it as inspiration for these design agents to come up with. Hey, this is what, not just a like for like translation of that old application, but also redesigning it for the modern world. Yeah, so no use doing that old thing or whatever kind of. So those are the design agents and then we actually have an application in Pega. We can actually import it into the platform and then maybe further connect to real data sources. Ah, that's where agents can come into play because they can help to identify like hey, these are ideally you would like the data model you would like to have in your application here for first notice of loss but what kind of physical data sources do we actually have access to? So they can further. And there's further types of agents that can help to then further develop that application, test automation or whatever it is. So then we have a running application. But maybe we want to coach. Yeah, a claims agent through some claims adjudication process. Right. So then it's more a coach agent that would figure out like, I mean, this is a claim that we can just probably readily approve or not, or whether we need to forward it on to be investigated or whether we can recover the claim, etc. And also connect the claims agent to different forms of knowledge, so called knowledge agents, where we can ask all kinds of questions and based on the policy and all kinds of rules and regulations and whatnot, we'll get answers to our questions. So these are examples of different types of agents that we can use when we develop these apps, but also when we run these apps.
- [12:28]
Podcast Host
And so maybe even just to keep on the insurance or the regulated industry theme here because I think that's a great. I think any brand wants to stay on brand and has certain guardrails. But I think in those highly regulated industries it's easy to talk about examples. One of the phrases in one of the announcements from PEGA at the conference is predictability over creativity. And it reminds me, creativity is an amazing thing, but you don't necessarily want it in some things like, you know, accounting or necessarily in insurance in all aspects or whatever. So why is that trade off so crucial for industries like finance, healthcare or even government?
- [13:16]
Peter Vanderputton
Yeah, yeah, no, I think that's a great question. Yeah, I'm maybe also of the creative sorts, but you cannot be creative if you also have some predictability.
- [13:26]
Podcast Host
Right.
- [13:27]
Peter Vanderputton
To belabor that metaphor a little bit, if there's awesome musicians out there, but the instruments that they play then actually need to be quite predictable. If I hit the chord or whatever, I don't want it to sound differently. If I play it exactly the same, then I can't be.
- [13:44]
Podcast Host
Or the timing creative.
- [13:46]
Peter Vanderputton
Jimi Hendrix.
- [13:46]
Podcast Host
Yeah.
- [13:48]
Peter Vanderputton
So you need to have predictable tools even if you want to be creative. But in these regulated industries. Yeah, in this, let's file an insurance example we can, where the agent maybe shines and is trying to figure out, okay, what kind of, for this particular claim, what kind of different data sources or knowledge sources or policy information is there out there? How can I combine all that context into figuring out how to proceed with this claim? But at some point you're hitting parts where you don't want to leave it to generative AI where you want to go through a fixed process of particular steps or you want to indeed invoke very bespoke specific business rules because they encode your policy or whatever it is. So there you can see that it's not so much this case of either or agents. We can get rid of all our workflows because we have agents now that's never going to work. We really need to actually empower these agents and give them very predictable tools like workflows or business rules or the more traditional machine learning AI to be used as part of that process so that you get these predictable outcomes. The more extreme variant of this is if we would only use that design time to actually create these workflows, then they could even completely lock them down and say, okay, we should always follow this particular process.
- [15:23]
Greg Kilstrom
Yeah.
- [15:23]
Podcast Host
Because I mean, at the end of the day, anyone that's used chatgpt even knows if you ask it the same thing, it's going to give you different answers depending on I don't know what, but it's going to give you different answers each time. We've heard of hallucinations and things like that. So you know, all of these things, you know, it's not just as simple as let's throw AI at the problem, you know, whatever. You know, that blanket AI term actually means, I think a lot of times it means generative AI in people's minds just based on the last couple of years. So this is where, you know, how, how does a platform like pegas ensure that transparency, I mean that transparency seems key as well as, you know, visibility and control, you know, in these again, admittedly complex and multi agent scenarios.
- [16:14]
Peter Vanderputton
Yeah, like, so it's a different level. So when we design these agents we can very tightly control like what kind of tools, data, information sources, what kind of knowledge a particular agent has access to. Yeah. And we could even have things like that particular, let's say an agent as part of the interaction kind of get into like we want to use a particular tool that permission needs to be granted by a human to be able to actually do so. Yeah. Based on what the type of tool is, if it's just like getting some information from a particular source, then it's maybe okay. But if we want to execute a particular workflow or process to kick in that's going to make changes to whatever the customer contract or whatever it is, then we probably want to have a human force, the human back into the loop and explicitly say, yes, go ahead and do this. So this is at the level of configuration of those agents. But also I think if we actually as and when we're using these agents. Forget agent for a moment. Any work you that happens in Pega, we audit that to the 10th degree. Right. So what part of the process are you, what kind of data are you using, what kind of actions you have taken? So for us, agents are nothing new because they're just another actor in the system. And we'll audit to the nth degree what it is what they're doing. Yeah. If I get a complaint three months down the line, I can go back and I can see what happened in this particular insurance claim. Yeah. And who did what? The human, the agent, what kind of information, which agent was used.
- [17:55]
Podcast Host
I mean, it sounds a lot like how you would, which I think should give companies a little, should make them feel better about this, is that it's kind of like what you would do with employees. Right?
- [18:07]
Peter Vanderputton
Exactly. Yeah, yeah, yeah. So, and I don't want to sound like that we were super smart. It's just, it's more. Yeah. We had to build it anyway. We first started with straight through workflow, but in very kind of regulated environments. Then it was like, oh, we also want to orchestrate not just straight through workflow, but people doing stuff and make sure that we audit all of this and that we keep a context of what's going on with this particular case. And then we're like, ah, we have real time decisioning. We need to be able to make real time decisions again, control what kind of data is being used, being able to audit and trace back all these decisions. So for us agents just, it's in that sense. Yeah, just another actor that gets involved into the mix. But they will be controlled and audited at least the same level, if not more, you know.
- [19:00]
Podcast Host
Yeah. So yeah, it's not like the machines are running amok or whatever. It's like an employee that doesn't take vacation or whatever.
- [19:09]
Peter Vanderputton
But now I make it sound like it's super easy, but actually without having that environment where you're doing that, then. Yeah. Then.
- [19:19]
Podcast Host
Well, without the guardrails in place, you're kind of just hoping. But with a system like this, and I guess the other parallel is just with the different types of agents, you are using the right tool at the right time and only within those parameters.
- [19:36]
Peter Vanderputton
Yeah, exactly. And these agents could also be. One agent could become a tool for the other agent. Right. So you can get quite complex interactions where you still want to have all the instrumentation in place so that you can actually see you know, this could be when I'm in the middle of the claims process, understand how we got to a certain decision outcome or recommendation or maybe three months down the line. Yeah. If you're in an audit situation or whatever. Yeah, love it.
- [20:02]
Podcast Host
So I know we're, you know, we're relatively early, early days here with, with some of this. Have you seen any early examples or feedback from clients using these predictable AI agents that, you know, some feedback there and how they're being used?
- [20:18]
Peter Vanderputton
Yeah. So what we always like to do is, you know, we're not the kind of company that like sometimes people maybe say, well you guys should be a little bit more, you know, let's say maybe first make some grand claims and then figure out later how it's going to work. But we're quite down to earth. We like to really get hands on and technical and try things out. So this Agentix service, we kind of, it was a bit of a skunk works. We kind of built it into the platform already, I think a year and a half to maybe even two years ago. And that allowed us to kind of get experience with different types of applications internally as well. Right. So one thing that we even prototyped, even in parallel to that was an intern. Intern, Irish. We gave her a name and you can ask her like all kinds of different types of questions. It has access to product documentation, customer information, whatever it is, and then she will address your question. And that's an internal application, but it's being used by a company of roughly 5 to 6,000 people and there's 1,000 to 2,000 inbound requests every single day. Mato Iris. So for us it was interesting to see that that's something that caught on like really early also because it's lower risk in the sense that the actions that it takes is gathering information from different sources and then addressing the question. So the worst thing that it can do is maybe provide the wrong answer. But it's not going to say, well, we're sending an invoice to one of our partners or whatever. So. So that's an example of a kind of an early thing. But we're also tomorrow Rabobank will be presenting as well as one of the keynotes. Right. So they're going to talk about some of the hackathons and early implementations they've done with agentec technology in the financial, economic crime area. Right.
- [22:16]
Podcast Host
And that's certainly something you want to, you know, you want to have those guardrails in place.
- [22:20]
Peter Vanderputton
That's a very good example of a highly regulated and confidential type of area. But they're heavily experimenting with all kinds of forms of generative, energetic AI. It started a little bit with this knowledge buddy with all the work instructions for the financial economic crime analysts. They have 3,000 of them. Right. And you can imagine it's an environment where this unstructured data plays a very big role and started with being able to answer questions on the basis of these work instructions. So that's more of a knowledge agent. But we also expanded to different areas like the latest thing they're also experimenting with and they went public about it. So I can say it is building up this full picture of a particular client that has raised some form of, well, either you're onboarding the client or there's some form of alert that came out of some system that triggers them to really have a look at whether everything is still okay in brackets and then building up that full picture, the first full picture. And it's also something where they were experimenting with agentic technology. That's also the way to get into it and just try it out. Yeah, yeah. Love it.
- [23:39]
Podcast Host
Well, couple last questions for you here. We're here at pegaworld here in Las Vegas where attendees can test drive some of this stuff as well. I'm looking forward to doing that in a few minutes myself. What do you hope that attendees walk away after experiencing some of this predictable AI firsthand?
- [24:00]
Peter Vanderputton
Yeah, no, I think I always love to demystify AI a bit and I think I don't believe when people say, well this agentic AI is just a fad, it's going to pass on and it's way too dangerous to use them anyway. I don't believe that. And on the flip side, where the tech bros are running around and shouting, just throw as many agents you want into into your company and magically, you know, they will solve all problems. I get the sharers as well. So I think it's really important to kind of demystify it and just show how do these things work. Right. So we're very experience based here at pagerworld. So all customers can just get hands on in their particular area, whether it's customer service or intelligent automation or one to one marketing and XD experience, you know, like how do these agents actually work? What can they do, what can't they do? And they can take that home. And then they can actually also get into hackathons or other ways to kind of get hands on with the technology and demystify it and start to understand what the strengths are and how to implement it in your own organizations and what things are maybe still too complex for these agentic systems.
- [25:12]
Podcast Host
Love it, love it. Yeah, like I said, I'll get my hands on it in a few here, so looking forward to that. Well Peter, thanks so much for joining again coming back to the show. One last question for you. What do you do to stay agile in your role and how do you find a way to do it consistently?
- [25:29]
Peter Vanderputton
Yeah, like I'm a little bit of an AI nerd, right? So it's not too hard for me to kind of keep being interested in it, but in general stay curious, stay curious and get real. Right. So I also go beyond just the hype around the topic and play around with the technology. That's a way get hands on with it. Getting hands on with these things is something that for me always also a good way to do this. Sanity check. What's behind it really. Like I said, I'm also at university, so keeping up with the cool kids is another way to remain agile and that's hard enough in itself. So yeah, love it. Love it.
- [26:13]
Podcast Host
Well again I'd like to thank Peter Vanderputton, Director, AI Lab and Lead Scientist at PEGA for joining the show. You can learn more about Peter and PEGA by following the links in the show notes.
- [26:26]
Greg Kilstrom
Thanks again for listening to the Agile brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the show@theagilebrand.com that's theagile brand.com and contact me if you're interested in consulting or advisory services or are looking for a speaker for your next event. Go to www.gregkilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled brand production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
- [27:19]
Liberty Mutual Insurance Spokesperson
The agile. And Doug, there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty Mutual. Even if it means sitting front row at a comedy show.
- [27:38]
Peter Vanderputton
Hey everyone, check out this guy and his bird. What is this your first date?
- [27:42]
Liberty Mutual Insurance Spokesperson
Oh no. We help people customize and save on car insurance with Liberty Mutual together. We're married. Me to a human, him to a bird.
- [27:48]
Peter Vanderputton
Yeah, the bird looks out of your league.
- [27:50]
Liberty Mutual Insurance Spokesperson
Anyways, Get a quote@libertymutual.com or with your local agent. Liberty Liberty Liberty Liberty Dish has been
- [27:58]
Dish Network Announcer
connecting communities like yours for the last 45 years, providing the TV you love at a price you can trust. Watch live sports, news and the latest movies, plus your favorite streaming apps, all in one place. Switch to DISH today and lock in the lowest price in satellite TV, starting at 89.99amonth with our two year price guarantee. Call 888-add-D dish or visit dish.com today.