Are you ready for the next frontier of artificial intelligence? 🤖
In the latest episode of Dell’s AI & Us series, host David Nicholson sits down with two leaders at the forefront of AI, John Roese, CTO & CAIO at Dell Technologies, and Dr. Pattie Maes, Professor of Media Technology, Arts & Sciences at MIT Media Lab. These industry experts share their knowledge on how Agentic AI autonomously makes decisions and performs tasks, acting as a collaborator to drive efficiency and innovation within businesses.
Highlights include:
🔹Defining the New Frontier: The conversation brings clarity to Agentic AI, exploring its capabilities to plan, reason, and act autonomously, redefining how we interact with technology.
🔹Reshaping Industries: Insights are shared on how Agentic AI is poised to revolutionize various sectors, from enhancing human-computer interaction to driving unprecedented efficiencies across enterprises.
🔹Dell’s Role in AI Advancement: John Roese provides Dell Technologies’ perspective on enabling this shift, discussing their strategic approach to advancing AI infrastructure and supporting organizations in realizing their AI visions.
🔹MIT’s Visionary Research: Pattie Maes offers unique insights from MIT’s pioneering work in intelligent interfaces and ubiquitous computing, highlighting the scientific foundations and future possibilities of Agentic AI.
Learn more at Dell Technologies and MIT Media Lab.
Watch the full video at Six Five Media, and be sure to subscribe to our YouTube channel, so you never miss an episode.
Or listen to the audio here:
Disclaimer: Six Five On The Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we ask that you do not treat us as such.
Transcript:
David Nicholson: Welcome to AI and Us, a series where we explore our future together in the age of artificial intelligence. In this episode, we’ll be exploring agentic AI, which represents the new frontier in AI innovation. It’s a new technology to many of us. Our goal today is to have a conversation that’ll bring clarity to this cutting edge topic, offering tangible insights into how agentic AI will redefine industries and evolve in the years ahead. I’m Dave Nicholson with the Futurum Group and I’m joined by two visionaries from Dell Technologies and MIT, both of whom stand at the leading edge of AI. First, we have Dr. Pattie Maes. She’s an award winning author, scientist and professor in the Media Arts and Sciences program at MIT. She founded and directed the MIT Media Lab’s Fluid Interfaces Group and works regularly with the MIT Artificial Intelligence Lab. Her areas of expertise are human computer interaction, intelligent interfaces, and ubiquitous computing, which I’m fascinated to hear more about, of course. Joining us once again on this podcast is John Roese, Global Chief Technology Officer and Chief AI Officer at Dell Technologies.
David Nicholson: Good to see you again, John. Good to see you, Pattie. John, let’s get straight to it. What is agentic AI? And I guess more importantly, what isn’t it? Yeah, let’s get some clarity.
John Roese: The world is a combination of digitized information, but it’s also a world of skills. And agentic is the approach of using artificial intelligence to actually digitize those skills, to not just create a tool for people to use, but to actually transfer the actual work, the reasoning, the behaviors into this machine layer that is now an AI system. Now, the result of that, if you do both of those, you’ve made a profound impact but what is an agent? An agent is the tool to do that second thing. And what it is is just a software system. It probably has a large language model that provides some communication and other tools to it. It might have a knowledge graph or some data set that it uses to kind of emulate memory and understanding and reasoning. But more importantly, it’s really the behavior of the agent that matters. It is not just a tool for humans to use, it’s actually something that you can give autonomy to. You can delegate a task to it, you can give it an intent, it can figure out how to do that work, it can ultimately accomplish that work. It can interact with you as it does that work if it has a question. And ultimately that system introduces an environment where the human is no longer in the loop exclusively, meaning making all the decisions. The human is on the loop. It is expressing an intent and letting the technology do the work, even if that work is narrow or that work can be very broad. More importantly, agents tend to have a couple of characteristics that are new. They have the ability to do what’s called function serving, which means they can reach out and interact with tools. So not only do they process data, but they can be told to interact with another tool to get new data or invoke a behavior in the real world. They’re also perceptive. Generally they’re able to observe the real world. You can give them access to sensors, and so they can sit there idly. If your system is self contained and cannot interact with the world around it in any way, it’s probably not an agent, it’s probably something else. Now all those other tools are useful and good, but agents are the new capability. And imagine, you know, what we can do with this Capability, as we think about it, in part of our tool, tool chest.
David Nicholson: Well, Patti, John mentioned this idea of the kind of interactions and collaboration that happened between humans and agents. That’s your area of expertise. What does that look like? Where are we now in this collaborative effort between humans and agents?
Dr. Pattie Maes: I’ve been arguing for over 30 years, actually, that we should change the way we interact with our devices and give our advisors a little bit more autonomy to take care of tasks on our behalf. Work and life are ever getting more busy and demanding, and whether it is in a work context or in our private lives, we all sort of need to multitask and take care of more and more information and process it and keep track of a lot of things. And so agents are a way to do that. They can help us with managing the overload of information that we want to keep an eye on and the multitude of tasks that we are sort of dealing with in parallel. So I think that increasingly, again, in private lives and in work life, people will be managing a collection of agents that help them with all of these tasks. Now, I would say that these agents, especially in the beginning, are not likely to fully automate tasks because you want to make sure that the agents actually do things the way you really want them to be done. AI still makes mistakes. We all know that LLMs, which are sort of the backend for today’s agents, still can hallucinate and don’t quite understand the world or still make reasoning mistakes. So it is important, in my opinion, that humans are still in the loop and that these agents sort of either are audited and report back on their behavior and that people check that especially, especially initially when they adopt agents into their work or private life processes to make sure that things all go smoothly.
David Nicholson: So it sounds like there’s sort of a continuum of a journey towards autonomous behavior here. So definitely a collaboration between humans and agents. John, are there specific sectors of the economy or industries that you see having specific challenges with this and then conversely, maybe, which are more well suited for this type of collaboration? And Patti, I want to get your feedback.
John Roese: The analogy I give is that they will have giant training wheels on them. They will be constrained in a way where only the things that we’re comfortable doing as we’re allowed to do autonomously. Now the nice thing is because the technology foundationally is quite strong. It’s just a question of its behavior and its data that we got to get comfortable with. We can relax those constraints over time. You know, your first project might only allow it to do a very, very specific thing with some degree of autonomy and everything else you’re involved in. And once you get comfortable with that, you might give it a little bit more capability and you’re just relaxing the constraints, giving it a bit more data, changing the prompt. Those are things that are very likely to be the pattern of deployment, regardless of industry. Now if we get into industries, what we discover is the risk profile of industries matter. For instance, in some cases we will see an industry that is heavily regulated and that industry will fundamentally require, that industry will fundamentally require a much slower graduation of autonomy in places like software development, funny enough, which is a very important space, but there’s not a lot of regulation on it. We’re seeing much more aggressive use of agents as long as we can predict the behavior and the outcome. So I think there isn’t an industry that won’t be impacted, but the path, depending on what industry you’re in, will be an industry in terms of how much risk is resultant from transferring trust granting agency to a technology to do things on your, on your part. By the way, today we have found no industries where there isn’t a starting point, where there isn’t something that they’re incredibly comfortable handing to an AI to do on their behalf if it’s constrained, if it’s got the guardrails, the training wheels and they can actually trust it and that once you get down that path, relaxing the constraints is far easier than building the first agent.
David Nicholson: Pattie, do you have a different view based on the human equation?
Dr. Pattie Maes: Yeah, well, I do think that there’s a huge role for agents, definitely, but we have to thread care carefully and I think starting with tasks that are well defined, constrained and like John said, giving the agents gradually more autonomy is really the way to go. But I think ultimately people always still want to be in charge of their agents and know what they are doing, know what they have been doing. So get reports, audit them regularly. We don’t want to lose complete control of our processes. And delegate them to agents without knowing what these agents exactly are doing. So I am very interested in that problem, sort of human agent interaction and making sure that we can trust them and that we still ultimately the agency or the control still lies with the person.
John Roese: Pattie. It’s a great point because we think about it, it’s more complex than you interacting with a bunch of agents. The agents interact with each other. There’s almost no scenario where AI operates in a complete vacuum. In the agentic world, you may find that you are ultimately the instigator of whatever you want to happen. A great example would be if you had a concierge agent that was very personalized, and was running on your local device. You completely trusted it, it understood your priorities, and then you delegated and said your job is to book my vacation. Well, it may be the thing talking to the travel agency that has an agent and the airline that has an agent, But to your point, the ultimate measure of success is does the human being trust that it is doing the right thing in the right way on their behalf? If that is not true, this technology will not be successful. If it is, it will be wildly successful.
David Nicholson: What are some of the emerging technologies that we’re all going to have to keep up with as humans that are going on right now?
John Roese: The biggest one that we’re really excited about is how do you make these things work with each other? You cannot do that by just hoping they work. You have to develop interworking standards. Today we have a lot of work around things like agent to agent and MCP and agency. And there’s a bunch of initiatives that are all coming together at MIT that is really exciting about dealing with registries. So we’re going to develop interworking protocols, orchestration models for these systems and all of those things are necessary. The exciting thing is most of them didn’t even exist six months ago and now they’re at a point where we can actually code to them, we can implement them and we can play with them. And so that tells us, if you ask me, what the important technologies of our agents are today, kind of answer. If you ask me in two years, I have no idea. This is a fast moving space. We’re going to invent all kinds of things, but it’s going to keep moving.
David Nicholson: What about, what about this December, Patti? Where are we going to be on this agentic journey by the end of this year? And remember, this is being recorded. So.
Dr. Pattie Maes: Yeah, personally, I think we’ll see early experiments in businesses, especially with tasks or domains where the cost of a mistake happening here or there is not too high. So I think businesses will have to be careful about what kinds of tasks and processes they first sort of try to automate or delegate to agents. I think in the sort of space of human computer interaction, I think increasingly we will be talking to an agent that is sort of like our concierge agent, as John called it, that knows us really well, knows what we care about, how we like things done and that can really sort of on our behalf, keep track of a lot of things and automate a lot of things and answer questions that are easy to answer, etc. So that we have an easier time sort of staying on top of our, say, huge inboxes and busy calendars and lives and all of the things sort of that we’re trying to fit into our lives.
John Roese: Yeah, yeah.
David Nicholson: I think, John, when you tell us, you know, where you think we’ll be by the end of the year, also maybe you could help us understand where we should start. A lot of folks are asking the question, how do I either save money or make money with AI, let alone just agents? But so help us understand where we are going to be by the end of the year and where should people be starting today? How do you get started?
John Roese: Yeah, how do I start? Yeah, I think. I mean, I’ve lived through it. We’re. We’re figuring out where we, you know, how we go on this journey. And here’s what we figured out. Change over time. But if I give you. I’ve given this advice recently to colleagues and people across the industry. Three things in this order. The first is agentic. AI is all about moving work to an autonomous system in the AI world. So the first step is to identify a piece of work that’s worth doing that to, that’s tied to something important, but not so important that your company will go out of business if you get it wrong. And so it’s a specific task that biases towards autonomy. It’s a specific area. Maybe it’s not in a heavily regulated area, but it’s still important. And if you did it, your business would run better, your software would be better. Don’t do science projects that have no meaning. Do pick something that actually makes sense, but is in that swim lane of not heavily regulated, not super risky, but still worth doing. Figure that’s the triangle you want to target as a starting point, which process you’re going after, which task. That’s the first step. The second step before you do any technical work is you have to figure out what we call the agentic workflow, which is, okay, now I’m going to take this task and tell a piece of technology to do it. If you understand agents, you’re not telling a singular technology in a vacuum how to do this. You are telling a system that has the ability to access tools, to perceive information, to interact with other agents. And so you have to kind of work all of that out. What tools will it need to do its job? What data will it need to do its job? What will it interact with? And that is not a technical effort, that is at the technical level, but it’s like mind mapping of how this thing should behave. And then after you’ve done those first two, then the most practical thing to do is to prototype that agent in a controlled environment on a standardized system system. And the reality is when you end up doing that, that will prove that that idea, which is valuable, actually works? And it’s that sequence. That’s what we’re doing in Dell. We’re not jumping right to technical experimentation. We’re trying to figure out what process we’re going after and what it means to create an agentic workflow before we even write a line of code or use a piece of technology.
And what my prediction is, by the end of this year, the first places where you will see agentic materialize, and it is the first place where it should materialize. So if you’re asking where you should start, don’t pick an entirely new AI workflow. It turns out that if you started to tackle the process of finding customers and selling to them like we did, you found that your first problem was in that process. Maybe I can make content management better by using generative AI to create one source of truth and one interface. Great, that’s perfect. But now you already have a process. And so say ask yourself, well, if I have agents, what would I do to make that even better? And we realized that, boy, you know, as you’re going through that process, there’s moments in time where you have to leave the process and go to a financial system and get a price or go to an inventory system and get an answer. Now you could hard code that in and do it the old way. Those are perfect agentic use cases. An agent whose only job is when the salesperson needs to get a price, it does it for them. That is a perfect agentic use case. And it complements the path of getting from finding a customer to selling to a customer. And so, same thing with software development. Don’t throw out your coding assistants and decide to go to agents orthogonally. Look at the things within your coding process where there’s a bit too much human intervention. It’s characterizable, you could incorporate as an agent. So by the end of this year, I think the manifestation of agents in the real world is not in an entirely new domain. It’s complementary to the first generation AI workflows that we’ve actually already put into place.
David Nicholson: From the kind of back to that human technology partnership. Do you see the starting place for individuals being a little different? I’m thinking of using agents to do things for me personally or as an individual contributor to the business, making me more efficient. Where would you recommend people start that journey?
Dr. Pattie Maes: The personal journey? Yeah. So I think again, increasingly, I mean, especially the younger generation that I work with at MIT, the younger students, they use today’s chatbots all the time, like at least two hours a day for solving all sorts of problems or questions, answering all sorts of questions that they have. And I think that especially that generation will also be very quick in adopting agents systems that go above just providing information, but actually automate some simple things on their behalf, whether it is answering to someone, are you available at this time on that date, or simple things like that, where the cost of a mistake isn’t too high, maybe it is ordering pizza from your favorite pizza place, etc. But I think they’ll still be fairly constrained in terms of what kinds of things they are allowed to do
David Nicholson: So baby steps. But the baby is stepping at 60 miles an hour is sort of what I’m hearing. It makes sense to have an approach, but it’s all going to happen quickly.
John Roese: Well, by the way, even in industry and companies we see the same pattern. Our younger and career people are more likely to embrace a different way of working, a different tool. And so it doesn’t mean that, you know, those of us who have been doing this a long time aren’t open to it. It’s just if you’re not stuck in your ways and somebody gives you a tool that can actually change the way you work in a really positive way, you’re likely going to lean into it. But our data does show that even the people that are most resistant as you roll these technologies out, if they work within six months, everybody’s using it. So it’s just a question of who goes first. It’s not a question if it doesn’t get adopted.
David Nicholson: Patti, I’m fascinated by something. You’ve been at this a long time. We’re talking about agentic AI, which you could argue is sort of the leading edge of what we’re doing with AI today. But you’ve been involved in this study of the thoughts around artificial intelligence for literally decades, decades and decades. What does that look like? Have the things that you thought about 30 years ago, dare I say how many of those things have come true? What sort of surprises have you seen? Walk us through that.
Dr. Pattie Maes: Yeah, so after getting a PhD in AI, I actually shifted my focus towards what I call IA or intelligence augmentation. I sort of had a sort of realization that I didn’t really want to make computers and robots smarter, but I wanted people to become smarter and more capable, et cetera. And that’s actually why I started talking about software agents in 1994 and arguing that we should change the way we interact with our computers so they actually do much more of the work on our behalf and help us manage our busy lives. Of course, back then we were prototyping some of these agents, but we had to carefully craft them by hand. And of course, what has happened now is that agents can rely on these foundation models, these large language models that know a lot about every task out there and what the different steps are that are involved, if you want to get pizza on your kitchen table tonight, etc. So we don’t. We no longer have to build these systems by hand. We can rely on LLMs. And that is what has opened up this whole discussion again, I believe, around agents.
David Nicholson: Have you been surprised by the pace of change either way? Has it? Often when we’re really close to a subject, we assume that things will happen at an accelerated rate. But in this case it’s over. Over the decades, what does that look like? Are we lagging behind your vision?
Dr. Pattie Maes: Well, it took 30 years, but definitely I barely can keep up the last couple of years in terms of how fast the world of AI is moving. And most likely that’s because there’s now so much investment that goes into it and a lot of the development is happening in industry. Of course, it used to be largely the domain of academia before that, and that was a little bit more of a manageable pace, I think. So what I worry about these days is that maybe we’re going a little bit too fast sometimes we’re a little bit too eager maybe to adopt these technologies without sort of truly understanding how they work or what their limitations are, how, what happens when we have all these agents talking to each other where the agents are not 100%, say, trustworthy, etc. So that’s definitely one of the things that I’m a little bit concerned about. Yeah.
David Nicholson: Do you, Are you more concerned today than maybe you would have been 10 years ago?
Dr. Pattie Maes: I am, definitely. I mean, for the last couple of years, the philosophy just has been like, if we can build it, we build it and then we throw it out there and encourage everybody to use it. And we’re sort of conducting an experiment in the real world as opposed to letting researchers sort of conduct smaller scale experiments to understand what some possible problems may be. That’s also why I recommend that businesses, for example, really think carefully about what processes in their business are not too critical and are very repetitive in nature, etc. So that they are maybe a better sort of opportunity to deploy agents that are carefully Constrained and so on, so that nothing can go wrong.
David Nicholson: So, John, you talk to CIOs and CTOs probably every day. And you, you and I both know that the big question is, you know, it’s not. It’s not just where do I start, but it’s how do I start? What should I do? I mean, how, how do you, how do you begin to approach that? Are people even asking the right question?
David Nicholson: Where I can imagine within Dell, your kind of professional services and solutions engineering folks are really being pushed to the forefront because so much of what you just described sounds like that mapping is absolutely critical.
John Roese: Yeah, that’s a great point. In fact, our PS organizations and our business transformation folks spend way more of their time on the non technical aspects of AI. It’s helping customers figure out what process, where to apply this, how to safely apply this. And you still have to do technical work. You can’t miss that AI is a technology and ultimately has to be implemented. But the order of operations matters. And I will tell you it’s insufficient to just do the consulting work because it has to ultimately get built and implemented. But it’s also unwise to do the technical work without knowing what problem you’re solving and how that problem should be best solved. So, you know, I think this is just another example of when you do modern technology, it’s always an ecosystem. It’s always multidimensional. It always has a human component to it. This is just like every other technology at this time. It just happens to be faster, more impactful, and more interesting. Yeah.
David Nicholson: Do they need to understand any of the tech?
John Roese: when we are evaluating or creating a path to do agentic, there is always a technologist in the room. This technology is not unbounded. It is not infinitely capable. It has risks. And if you’re developing, even if you’re picking the process, you should do it with a conscious understanding that there is a technology implementing this and it has boundaries. But we view that you cannot hand off a purely intellectual concept into a technology ecosystem without ever connecting those two. And the best way to avoid it is to have the technologists and the business people at the table at the same time for the entire process. It will just work better.
David Nicholson: Well, it’s interesting. It feels like a lot of this keeps going back to Patti’s area of domain expertise, which is this intersection between technology and people, because you’re describing it kind of from. From. From a different direction, but you know, it. There is no such thing as just business people figuring this out. There is no such thing as just technologists figuring this out. Would you agree?
John Roese: It is incredibly likely that within the next two years, whatever you do today, whatever your work is defined as the amount of effort that you have to expend to do, it changes profoundly. And a huge portion of that work that quite frankly could be automated, could be delegated, but can’t be because there’s no one to delegate it to suddenly is. And you fundamentally scale. That’s good.
Dr. Pattie Maes: Increasingly, our entire lives will be mediated by AI. The way we work, the way we learn, the way we take care of our health. We will have personal AI systems that know about our goals, that know what we care about, and that help us by automating a lot of the actions and the tasks on our behalf and keeping track of a lot of things on our behalf.
David Nicholson: Yeah, absolutely. I think all of us here will be embracing the agentic revolution as we move forward. John and Pattie, this has been a delight as always. This is a conversation that could go on for many, many hours. We thank everyone for joining us in this conversation. This is a conversation about artificial intelligence and when it really comes down to it, this is all about AI and us. I’m Dave Nicholson for the Futurum Group. Thanks for joining us. Stay tuned for further exploration into this subject in the future.
Author Information
David Nicholson is Chief Research Officer at The Futurum Group, a host and contributor for Six Five Media, and an Instructor and Success Coach at Wharton’s CTO and Digital Transformation academies, out of the University of Pennsylvania’s Wharton School of Business’s Arresty Institute for Executive Education.
David interprets the world of Information Technology from the perspective of a Chief Technology Officer mindset, answering the question, “How is the latest technology best leveraged in service of an organization’s mission?” This is the subject of much of his advisory work with clients, as well as his academic focus.
Prior to joining The Futurum Group, David held technical leadership positions at EMC, Oracle, and Dell. He is also the founder of DNA Consulting, providing actionable insights to a wide variety of clients seeking to better understand the intersection of technology and business.
