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Unlocking the Power of Multi-Agent AI in Enterprise Decision-Making
The surge of interest in multi-agent artificial intelligence (AI) systems marks a pivotal moment for business enterprises. As company leaders grapple with exponential data growth and increasingly complex decision-making processes, understanding and leveraging multi-agent environments could be transformative. This article delves into how multi-agent AI systems can revolutionize enterprise decision-making and what business leaders need to know.
The Rise of Multi-Agent AI Systems
In the current landscape, large language models have captured much of the spotlight, yet the concept of AI agents is rapidly gaining traction. Unlike traditional AI models that operate primarily on input-output mechanisms, agents are designed to act with a degree of autonomy by leveraging tools to perform tasks and make decisions. This shift from passive data processing to active decision-making is what distinguishes agents from mere large language models.
Why Now? The Perfect Storm for Multi-Agent AI
Several factors converge to make this the ideal time for multi-agent systems to ascend. Technological advancements have provided more robust processing capabilities, while the API and microservices ecosystems have matured to support seamless interaction between agents. These elements, coupled with sophisticated large language models, enable agents to understand and act upon complex, multi-faceted instructions conveyed in natural language.
Multi-Agent Environments in Practice
Business operations can be significantly streamlined by implementing a multi-agent system. These systems effectively break down operational silos, offering a more cohesive and agile approach to enterprise functionality. A multi-agent intranet, for example, can revolutionize internal company processes by reducing inefficiencies. Imagine expressing a need like a change in benefits due to a life event; an agent system would streamline this process, autonomously contacting the right department agents to initiate necessary adjustments, while proactively suggesting complementary changes, such as time off to celebrate.
The Dual-Edged Sword: Considerations and Challenges
Empowering AI agents with autonomy also requires caution. Leaders must ensure these systems are built with safety and governance at their core to prevent potential pitfalls. Compounding errors in decision-making, alignment issues between disparate agents, and the risk of losing strategic control are critical concerns that warrant careful attention. Companies should enforce a mechanism where redundancy and human oversight are integrated, ensuring any deviation is caught early and adjusted.
Steps Towards Implementation
As companies look to integrate multi-agent systems into their operations, they should focus on first identifying key areas where these systems can have the most immediate impact. Customer support, backend operational efficiencies, and data-driven decision-making can be the low-hanging fruits for leveraging AI-driven agents. Moreover, companies need to foster a mindset shift within their teams, encouraging innovation and adaptability in using AI in day-to-day operations.
Final Thoughts: A Call to Action
In a world where data is king, and information moves at the speed of light, multi-agent AI systems offer a new paradigm of efficiency and innovation in business operations. For decision-makers and business leaders, embracing this technology strategically can lead to enhanced productivity, greater operational excellence, and new market opportunities. Now is the time to learn, adapt, and lead the way into a future where AI and human ingenuity converge for unparalleled success.
Topics Covered in This Episode
1. Understanding Agents and Large Language Models
2. Implementing Multi-Agent Systems
3. Hallucinations and Errors in AI Systems
4. Usage and Organization within Multi-Agent Environments
Podcast Transcript
Jordan Wilson [00:00:16]:
It's no secret that agents are already the buzzword of the generative AI world in 2025. But what if we zoom out and even just think of agency decision making process? What does that mean? Should we be handing over as large enterprise companies? Should we be handing over that agency to AIs, to large language models? And not only that, what happens when you do that and you get some success and you want to scale and you want to be working in multi agent environments because whether you've realized it or not at your company, that time is today. So today on Everyday AI, I'm excited to have a guest, with a great wealth of experience in this area who's gonna be help walking us through and inside that multi agent AI and rethinking enterprise decisions. So welcome to Everyday AI. If you're new here, my name is Jordan Wilson. I'm the host, and we do this every single day. It's a daily livestream podcast and free daily newsletter, helping us all learn and leverage generative AI to grow our companies and our careers. If that sounds what you're doing and what you're trying to do, this is your new home.
Jordan Wilson [00:01:31]:
Your other home, that's our website, youreverydayai.com. So there you can go sign up for our free daily newsletter. We will be recapping today's conversation as well as keeping you up to date with literally everything that you need to know to be the smartest person in your company at AI. And, you know, we have hundreds of episodes with some of the brightest minds literally in the world on AI, and today's conversation is no different. Alright. So, enough of my chitchat. If you're here to, you know normally, we go over the AI news. This is technically prerecorded, debuting it live.
Jordan Wilson [00:02:04]:
So if you want that AI news, it's gonna be in the newsletter, so make sure you just go check it out there. Alright. Enough chitchat from me. I'm excited to bring on our guest for today. So please help me welcome Babak, the CTO of AI at Cognizant. Babak, thank you so much for joining the Everyday AI Show.
Babak Hodjat [00:02:21]:
A pleasure. Would love to be here. Thank you for having me.
Jordan Wilson [00:02:24]:
Oh, this is gonna be a good one. So before we get into your background in AI, which is extremely impressive, can you walk us through, I'm sure most people are aware of Cognizant, but for those that aren't, can you just tell us a little bit about what you all do and what your role as CTO of AI entails?
Babak Hodjat [00:02:40]:
Yeah. Cognizant is one of the world's foremost, technology services companies. We're based in The US, but we're everywhere around the world. We're more than 360,000 employees, and we are an AI first company, and, pioneering in the fields of AI and multi agent systems, and working with, clients in all sorts of different verticals. And, yeah. You know, I I joined about six, seven years ago, and it's a very large company, but it has a culture of a startup. I'm a startup guy myself. So that's part of the reason I've stuck around here.
Babak Hodjat [00:03:16]:
It's it's really cool to be working here.
Jordan Wilson [00:03:18]:
Yeah. And and speaking of, you know, agentic AI environments, you know, can you tell us a little bit about, Cognizant Neuro? It's been what, like, a year and a half now. How is that going so far? What does it entail, you know, for those that aren't aware?
Babak Hodjat [00:03:34]:
Yeah. It's going really, really well. We launched, Neuro AI, as you said, about, a couple of years ago. And it's because we recognize the fact that people are moving from, you know, give me the insights and I know how to make the decision to, you know what? Give me some recommendations. Like, it's not just the insights. It's give me recommendations because making decisions in, the presence of a lot of data and in a multi objective way is very, very difficult. And so that was the premise for creating neuro AI. And, you know, it was, it's a very technical platform and, you know, not too many people know how to build decisioning systems.
Babak Hodjat [00:04:18]:
So it had a whole certification program and so forth. We would quote about ten to twelve weeks to create a POC. What happened last year was that we started agentifying the platform. So agentification, you can think of it as, you know, taking the modules in your software and replacing them with agents that talk to each other. And believe it or not, that POC period of ten to twelve weeks is now down to ten to fifteen minutes.
Jordan Wilson [00:04:47]:
Wow.
Babak Hodjat [00:04:47]:
So it's it's simply amazing, and just talks to the power of, agentification. And then we've, of course, now started looking at how you could expand that beyond just software and whether, you know, an organization itself could be multi agent based.
Jordan Wilson [00:05:04]:
So before we get into the details, which I want to, this is something, I'm personally giddy to talk about, but let's hit rewind. Like, what the heck is an agent? Right? I I I think it's a word now everyone throws around. I feel it's like when two or three years ago, public companies just, you know, said the word AI as many times they could in their earnings call, but now everyone's just saying agents everywhere. Like, what the heck is an agent, and and what's the difference between, you know, a traditional, like large language model and an agent?
Babak Hodjat [00:05:33]:
That's that's a great question. You know, we were some of us, AI folks were at a round table at Davos a couple of weeks ago in Switzerland. And we were talking about how last year at Davos, you would walk down the promenade and everybody was talking about GenAI and we figured, you know what, these CEOs are going to go home and say, you know, I want GenAI by the way, what is GenAI? And now it's the same for agents. Like you walk down the promenade and everybody's talking about their agents and the CEOs are probably now back home asking their, technical folks, you know what? I really need this agent thing. What is it? So great question. So, agents have been around. I worked on multi agent systems in the nineties. And, the fact is that, if you take an AI system and give it some tools and some level of autonomy in deciding when and how to use its tools, we can then call it an agent.
Babak Hodjat [00:06:26]:
It looks as it's at its environment, at the task at hand, at its job description, and then decides how to use its tools to fulfill whatever, responsibility or task is it has been given. The reason be for this resurgence today in popularity is because by using a large language model as the brain of an agent that allows it to reason using the reasoning of these large language models. And, also it understands language. So you can just simply express to it what its responsibilities are, what the scope is, what you expect it to do. So intent driven, and then it can decide. And more often than not, it makes good choices as to how to use its tools. So the modern day agent, has a pretty powerful brain in a large language model. When you think of the large language model, that's just a model.
Babak Hodjat [00:07:22]:
You give it some inputs and it produces some outputs. It doesn't really do stuff. It's pretty general purpose usually, but, you know, it doesn't really do stuff. The moment you recast it as an agent, you write some code outside of that model that allows the system to decide. So it actually enacts the decisions that the large language model is asking it to do. You know, one example I use is we all know large language models are pretty good at writing code. We have all these get, like, copilots and so forth. So if you ask a model to write some code, it will write some code.
Babak Hodjat [00:07:57]:
And, you know, if it's not too complex, you can take that code and run it and work fine. If you have an agent and you've consciously decided that that agent will have as its tool set, this ability to run code in a container and see the result of that code. Then the same task of writing the code suddenly becomes much more tractable because the LLM can say, hey, I want this piece of code that I wrote to be run, and then the external code within the agent runs it, shows it the result. Maybe there's a runtime error or something, and it can correct that and iterate on it before it comes back to you and says, here's the piece of code that you were looking for. And by the way, I actually ran it. Here's a sample run. So right there, you can see same large language model, but one of them is more powerful because it has agency. It can actually use tools.
Jordan Wilson [00:08:50]:
So, Vivek, you said that you've been working on multi agent environments since the nineties. So, I guess, why now? Why is this, you know, at the all, you know, at the all time high? Is it just because the the advancements in in large language models? But why today in, in, in the very near future is the multi agent environment going to be a huge driver in the enterprise?
Babak Hodjat [00:09:16]:
It's just happening organically. And that's because, you know, the whole structure, the underlying requirements are all there. It's just all come together. When you look at the late nineties, when we started working on agents and multi agent systems, we had a lot of missing pieces. Like we, we didn't have the processing capacity, The world wasn't like a bunch of API and microservices like it is today. And you know, you didn't have, you know, the luxury of a model that could understand your natural language. So an agent was much, much weaker and more in the lab than what we have today. So, but why today? So when people started looking at large language models, immediately, they're like, oh, why We can do co pilots.
Babak Hodjat [00:10:03]:
We can have these large language models help us edit stuff or do stuff for us. The moment you start thinking about doing things, in other words, allowing whatever the large language model suggests to actually be actuated in the world, you have an agent. And so over the past year and a half, two years, organizations have started to build agents already without even calling them agents. There are these one off large language models that do certain stuff for us. You know, one of them is extracting data out of unstructured, you know, documents. The other one is, I don't know, editing something or filling out a form for you. The reason why I'm saying it's organic is because it's very natural for you to want to have these agents to talk to each other, because why would you now have a Rolodex of agents that you've implemented in your organization have to know which one to go to talk to? Like, why don't they talk to each other to figure out which one of them, or more than one is responsible for handling some, you know, input that you give them some, some inquiry, some command. So it's kind of happening organically right now, and we're starting to see, companies, you know, demand multi agency for, for their operations.
Jordan Wilson [00:11:22]:
And, and I think sometimes people think that this is a, a highly technical endeavor and in many cases it is right. But, for for listeners of this podcast, I did this recently. Right? I did it live here on the show. I had, ChatGPT's operator go out and use, Gemini and then send an email. Right? I had it do some tasks that I would normally do. And so, you know, very simple but multi agent environment. So, you know, as we talk about doing this at scale and and at the enterprise level, what are both the promise of the multi agent environment, and then what are some of the challenges or the things that, you know, business leaders really need to consider?
Babak Hodjat [00:12:04]:
Yeah. The promise is breaking the silos, making things much smoother and, operationally more productive and efficient. Because, you know, regardless of the entry point, you have a whole organization of agents, each responsible for some task within, you know, your organization that can listen in on your requests and given whatever, you know, authorization you have and so forth, they can come together and do something complex that would otherwise have taken you a long time, lots of forms filled, picking up the phone and try to find people. I'll give you an example, like at Cognizant right now, we're identifying our intranet, which is a collection of many, many apps. You know, we're so large that our HR department has its own IT department, You know? Our finance department. Right? And each one of them has come up with their own agents. So we're actually incrementally plugging these agents together and creating this multi agent intranet. Now Interesting.
Babak Hodjat [00:13:07]:
There are certain things you can do here that you can do very easily. I can go in and say, hey. My laptop has an issue, or I need I need Copilot for, this and that. You know? How can you provision me? And very quickly, it finds the agent that's responsible for that, you know, copilot provisioning. It checks your authorization and gives you a form and prefills it and you can click. Okay. Great. That's easy.
Babak Hodjat [00:13:28]:
But what if I had, like, a life change event? Just recently, my son turned 26. Right? And I didn't know what to do, where to go in the system for that. So all I had to do is type in my son just turned 26. Just imagine typing that into a search engine on your internet. I mean, you're not gonna get anything useful, but the system kinda looked around and it's like, okay, your payrolls are gonna change. Your benefits are gonna change. And, hey, by the way, congratulations. If you wanna take some time off to celebrate your son's, you know, 20 birthday, I can provision that.
Babak Hodjat [00:14:00]:
Like, I can take that time off for you as well. Things that, you know, off the top of your head, you're thinking, wow, you know, yeah, there, these are, some of these are very useful for me and they're coming from disparate sources from different departments. So that's the promise. That's, that's the amazing promise. The peril though is you don't wanna leave this to happen organically. I mean, it is right now, but you really wanna have control over this. Remember with agents comes this deferral of autonomy. So you're actually asking a large language model based system to take some decisions autonomously on your behalf.
Babak Hodjat [00:14:40]:
So you want to make sure this is done responsibly, safely, and in a manner where if stuff breaks, you can actually fall back to a rule based, you know, mechanism. So while it's not very difficult to connect agents to each other, it is rather dangerous because very quickly you lose control over what's happening in your organization. And, so exerting that control, making sure it's designed. One last thing I wanna say here is with agents and multi agency, we're moving back into an engineer discipline. With large language models, you know, we kept thinking, okay, this this is one model that does everything. So I just express my intent and it'll it'll do something for me. The moment you break it into smaller agents and for each agent, you give it some microservices, some API, some data to be responsible for, you have to consciously decide, you know, what is the subset of tasks I'm gonna give to this agent, and where am I gonna defer to it to do something autonomously? Where am I do I wanna be part of that process? I want it to ask me. So that kind of engineering, that kind of design needs, you know, thinking needs, for us to know what the process is.
Babak Hodjat [00:15:58]:
It it's kind of a holistic, perspective and safety and responsibility is super, super important here.
Jordan Wilson [00:16:05]:
So speaking of that, safety and responsibility. So, you know, you said in the instance where if something fails, you need to be able to fall back to something more rule based. Right? And I keep thinking of, you know, quote unquote early in the days of large language models. Right? Like, this is decades ago, but, you know, at least, you know, after the ChatGPT moment when everyone's talking about it, there was so much of an emphasis placed on hallucinations. Right? Now, I don't know, maybe maybe I talk about AI too much. It doesn't seem to me as if there's that much, attention being paid to what happens now in that multi agent environment. Right? Because everyone was worried about hallucinations, but I don't know. Is it the acceleration is too fast? Is the opportunity too great that maybe people aren't worried the same way about, like, agent alignment that they were initially about, you know, hallucinations from a single large language model?
Babak Hodjat [00:17:03]:
Well, they should be. The hallucinations have not gone away. You know, with progress, in building these large language models, we can reduce them. We can never guarantee that we are eliminating them. So, one of the things we're doing is we're sickly, giving away consistency in our engineered systems in favor of getting robustness. So it's consistency versus robustness. And we gain a lot by getting that robustness and the autonomy that we get from these systems. But we have to be very mindful of the fact that, you know, you might actually ask the same query from even an agentic system and get different answers.
Babak Hodjat [00:17:46]:
Like nine times out of 10, it'll work the same tenth time. It might be different. There's a reason why there's a regenerate button on ChatGPT and Claude and everywhere else is that's exactly because of that. Now remember though that if what you're doing is like what OpenAI is doing with operator, like one LLM that does everything, then you're much more subject to confabulation or Mhmm. What's commonly known as as hallucinations and the inconsistency. Whereas if the same task is broken into smaller tasks, like I've got one agent that's helping me, you know, look at hotels. Another one that's helping me look for, you know, various different destinations. Another one that's looking at pricing.
Babak Hodjat [00:18:32]:
Now I've actually reduced the scope of each one of these agents. And by reducing the scope and being more explicit about what I expect the LLM to do, I'm reducing the, you know, inconsistency and, hallucinations as well. So multi agency actually helps us reduce that. Again, we're not eliminating, eliminating, but we are, we are reducing. But there are other techniques as well. Actually, our lab just came out with a, with a technique. We, we had a paper at nerds just recently where we can actually measure uncertainty in the output of a large language model. This is a huge breakthrough.
Babak Hodjat [00:19:12]:
Right? And so you can actually ask the system to give you, like, 10 responses and tell you what its confidence is for the same input on those 10 and pick the one that has the highest confidence. That significantly reduces the inconsistency and hallucinations, but it still doesn't eliminate it. We there's no guarantees.
Jordan Wilson [00:19:35]:
So one thing that, you know, I'm always thinking about, and I've talked about it with a a couple of guests before on this show is, you know, let's say an agent a single agent is one degree off target. Right? In if if that goes for a while unchecked, that one degree is very far off the destination. But generally, in one human, one agent scenario, the human is hopefully paying attention and doing their good human in the loop job. Right? And making sure that that, 1% goes as close to zero as possible. What about the compounding factor, though, when you have a multi agent environment? Right? I almost think of it as, you know, the someone in an air traffic controller, and there's all these planes flying at once. They can't possibly give, enough attention to all of them. So how is that gonna work, and and how should, you know, the CTOs, CEOs, be, addressing this issue of kind of this alignment or this this compounding? You you know, if an agent is 1% off and they're working in a, you know, a swarm of agents. Right? So how do you see that playing out and what should business leaders be doing?
Babak Hodjat [00:20:41]:
Yeah. You know, the way we deal with that with humans, I mean, humans are these black box intelligence systems that do I mean, we don't call it hallucination, but they are inconsistent. Sometimes they are erratic. They do things that we didn't expect. And so how do we deal with them in human organizations? Redundancy. Like you actually have more than one person checking each other's work, double checking, triple checking. And so that whole, you know, error error, compounding that you're talking about, that actually helps reduce it because you have multiple agents checking each other's work. So we can we can do that.
Babak Hodjat [00:21:19]:
You know, when not large language models came out, some folks, even within the AI community, were like, oh, there's this compounding issue because it outputs one token at a time, and there's no way that it's gonna remain consensus after a while. That's a fallacy, though. Remember that as it's moving its window forward, it might have actually come up with one token that's kinda off. But then when it actually looks at that token along with all the other tokens and gives you the next output, it can correct for that. And so that's something that they didn't think about. The same kinda goes with multi agent systems. If you want your system to be safer, beyond the fact that, as I said, you can, you can add rule based fallback and you can have human in the loop. All those things are, are granted and great, but you can also build redundancy into the system that does make it cost more and it might slow things down, but there are certain processes where, yeah, you would willingly pay for that extra cost to make the system, you know, more resilient that way.
Jordan Wilson [00:22:21]:
Mhmm. So, another thing that I I I think is worth talking about in these multi agent environments, I don't know. I feel in, you know, maybe 2023, maybe 2024 even, you had teams. Right? Small teams were, you know, working with their group of ten, twenty, 50 and trying to find the the one, you know, AI solution or the one large language model that could really help, propel them. You know, so team of a lot working with one. Are we now gonna be flipping that on its head now? Is it gonna be, you know, one person working with 50 agents or is it still gonna be, you know, groups of individuals working with groups of agents? How might that work out?
Babak Hodjat [00:23:04]:
Yeah. It's hard to say. And I think it depends on, on the use cases. I do think that we would all benefit by by getting into the habit of, you know, creating and using our own little team of agents that that would do stuff for us. Let me give you a quick example. We have this, this thing we do at at our lab called the FedEx day. It's like a twenty four hour, you know, FedEx. We deliver basically like a hackathon.
Babak Hodjat [00:23:32]:
And I was thinking, look, I get a lot of email that comes in and I play the router. The email comes in and I'm thinking, you know what? Let me send this to my product manager. Send me send this to my head of research. You know, let me get their opinion and stuff like that. Can I automate that? So what I did was I actually replicated our lab, like, the hierarchy of our lab in agents. Right? And then I had as the email comes in, I would get the email to the top agent. The top agent would check for spam, would check for urgency, and then it would actually distill the email and send it down to the hierarchy. And it was as if my team is looking at the email.
Babak Hodjat [00:24:11]:
And they would come back sometimes even, you know, giving me, like, the text of the email response that they thought I would be sending back. So even at a personal level at work, I think that one to many, as I described right now, will make sense for a lot of stuff that we do right now so that it frees us up to do, you know, the real work. But having said this, there are processes where we will have to have, you know, the human in the loop on a agent per agent basis. Like, the agent cannot make the decision. It can give recommendations, but it can't actually move ahead with the process without actually making sure that the human is taking a look at that. If anything, because we need a responsible party in this process. We need someone that takes responsibility for the action. Even if we believe that the AI would take a better decision than the human, right now, society is really not ready to defer, completely to to to machines for for many types of decisions.
Jordan Wilson [00:25:17]:
Yeah. And and speaking of, you know, decision making and machines, you know, I'd I'd kick myself if I didn't ask you this. And for, you know, our audience that is isn't aware, so you were the, you know, co co inventor of a technology that essentially led to Siri's development. Right?
Babak Hodjat [00:25:34]:
Right. Yeah. The natural language technology behind Siri.
Jordan Wilson [00:25:37]:
Yeah. So, you know, so you have many decades of of of experience. Right? I I look at this transition that we've kind of been going through over the last couple of months, right, or at least, in in popular technology terms. Right? But, how we kind of gone for this, robotic process automation, the RPA to the to the CUA, you know, computer using agents. But I don't know. To me, it still seems almost like archaic. Right? Like, it it still feels to me almost like RPA where it's like, oh, I'm having to click this, quote, unquote, record this even if I'm recording something, you know, through a large language model. Is it gonna get to the point where we're just like this using our voice? Right? Natural language right? Natural language processing to, you know, a multi agent, kind of, workforce.
Jordan Wilson [00:26:26]:
Is that where we're going? And if so, how long or or what needs to be accomplished before I'm just talking to a group of agents that are helping me accomplish my day to day tasks? Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on GenAI. Hey. This is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to youreverydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI.
Babak Hodjat [00:27:45]:
That's that's a really good question. I don't know what that mode of interaction is going to be. And it might actually be different depending on the use case. I think, I mean, having been involved with these, you know, conversational systems for many years, one of the things I found is that while we seem to be okay talking to our car or talking to our dogs, we don't quite find it natural to talk to our, like, I don't know, a cylinder in the corner of the house. You know? So anthropomorphic anthropomorphizing, seems to be okay as long as that being is like moving around and has eyes and, you know, just subliminally, we consider it an animate, you know, intelligent being. So, and there are many, work situations where, you know, I don't know if it really makes sense for us to build that kind of humanoid, sort of companion just to make the interface more natural for us to just talk to it as if we're talking to someone else. It might be. And there is a line of thought that says we might want to consider not doing that.
Babak Hodjat [00:28:56]:
And actually, always, there was actually something, yesterday I was reading about how we should regulate so that robots don't speak like humans. They actually speak mechanically like, like old, science fiction movies, just so we know that we're not talking to, you know, a human. And so there is this line of thought that says, yes, use the intelligence in the box, but there may be interfaces that, yeah, have text in them and you can express intent, but there might be you know, in the old days when we were in the pre Siri days, you know, in our, you know, youthful exuberance to, to bring intelligence to the world, you know, we had the system that could work the TV set on the DVD and the, and the lights and stuff, but it felt so silly to say, like, turn the lights on and volume up, volume up, volume up, volume up. And that's, that's just stupid. Like just turn them up, you know? So there are certain things in the interface that, you know, we've come a long way since just, you know, simple text based interfaces.
Jordan Wilson [00:30:01]:
So, you know, you mentioned two great, use cases or examples of kind of a multi agent environment. So, you know, kind of the Internet example and, you know, HR agents kind of helping you, you you know, handle a query and and then the the the email example that you just gave. What do you think whether it's internally or or things with clients or things that you've seen, what do you think are gonna be some of the first or best use cases, that kind of go, quote, unquote, mainstream in the enterprise world of this multi agent system?
Babak Hodjat [00:30:34]:
Yeah. I mean, there are some obvious ones, which have to do with the entry point, maybe a consumer talking to a business in a b two c setting, like, like, you know, support, line and and so forth. These are very obvious cases where we've been working on this forever, and these agentic systems can even make them more pleasant and more productive. There are internal use cases where an employee needs to get access to something in the organization. The organization is huge with many moving parts. You can't simply know everything about what everybody is responsible for on doing. And so that's the Internet example. There are other processes.
Babak Hodjat [00:31:16]:
There are, cases where, you know, you basically look at the existing organization and the nodes, you know, the organizational chart literally, and you go, okay, here I have, various different, responsible, nodes, that are human driven? How can I augment them and then connect them just like they're connected in in, the world right now? And then give access to every single node in this organization to be able to use their sort of buddy agent as the entry point into the entirety of the enterprise. So I think we will see a lot of that coming in, but I think the first set of use cases are gonna be the more natural use cases that grow out of areas that we've been exploring already. And, you know, we will, we'll see a lot of productivity out of that just by creating, these, connected agents together. Let me let me just say that, we're we're we we are sitting on a bed of microservices and API already. So the tools are there. The organization and the processes are there to be discovered, but they can, be agentified. And then you have so in all of these cases, we're talking about fully aligned multi agent systems. So, you know, it's your organization, so all the agents better be working together and be friends.
Babak Hodjat [00:32:43]:
But if I have an agent network that on my behalf, a consumer or a b two b or something is supposed to go talk to another business, that's where, you know, that alignment, kinda breaks down. And so we will move into a a case where agents are actually agents. Like, they're representing us or our organization, and communicating with other agents representing other people in other organizations. And, you know, just recently, I I I did a, an article on this. I actually created this multi agent system similar to operator, but but multi agentic that helps with decision making. And I connected it to agent networks for, you know, some travel sites. And it was amazing just to read the inter agent communications because it's all English. You you can read what the agents are saying to each other.
Babak Hodjat [00:33:37]:
And so it would come back and say, hey. You know what? I got this deal from Airbnb that that's beating what you're giving me for, you know, a weekend stay in San Francisco. You know, can you do better? So this is like my agents talking to the, I don't know, Expedia agents and trying to come up with a better deal for me. Yeah. So that's that kind of gives you a sense of where this thing might be going.
Jordan Wilson [00:33:57]:
Yeah. It's like it's like have I'll have my agent go, contact your agent.
Babak Hodjat [00:34:01]:
My agent is gonna call you.
Jordan Wilson [00:34:03]:
Yeah. And then, like, I was actually having this very similar conversation with someone the other day about, like, human in the loop. And I'm like, I think it's gonna turn into, like, agent in the loop where you have, you know, certain agents that are kinda like your direct rep right? Or they're direct towards you, and those agents are talking to other agents, and you're just checking in with one. So that's not a crazy idea then. Right?
Babak Hodjat [00:34:24]:
No. I don't think so. I I I think, like, it's only crazy in that we're not used to it just yet, but I think we will get to a point where we're, you know, this is a knowledge worker in a box. Like, how cool is that? You can set it up to do whatever you want it to do. So, yeah, I think we will be and and you can program it using your own language. Like, the the barrier to entry is very, very low. So we should all be thinking, you know, what kind of agent would I love to have right now for what I'm doing in my work, in my daily, I don't know, my hobby, my entertainment. Let me go build one or a few agents to do that for me.
Babak Hodjat [00:34:59]:
Like, why not? I I think that will come. Maybe it's generational. Nope. Maybe it'll take a while. Maybe maybe the elements need to be a little bit more powerful, but it will come.
Jordan Wilson [00:35:08]:
Yeah. Alright. So we've covered a lot in today's conversation, but, maybe here as we wrap up, what is your one most important takeaway that you think business leaders, need to know when it comes to, you know, the future of enterprise work in this multi agentic AI system?
Babak Hodjat [00:35:26]:
Yeah. I think we want to liberate our employees to really spend time thinking about how they wanna use their tools versus actually filling out forms and, you know, that that whole, you know, grind of, you know, figuring out who to talk to and what to do. So I think I think this actually will will, make organizations much more agile, and, much more fun, quite frankly, to work on in in organizations like that. So if you have that as your vision of where you wanna take things, I think you need to start thinking, of agents, not just to, for developer productivity or for your call center. You, you really need to think about it, at an enterprise level and, and strategically.
Jordan Wilson [00:36:14]:
So good. Vivek, Thank you so much for joining the Everyday AI Show. This is an instant this is one of those instant replays. So if you're listening to this now, you gotta bookmark, come back and listen to this again in in three to six months. I guarantee you, it is still gonna be a gem. So thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.
Babak Hodjat [00:36:33]:
My pleasure. Thank you for having me. All
Jordan Wilson [00:36:36]:
right. A lot that we covered there in a little time. So, don't worry. Maybe you were, at the gym or on a walk and you missed something, that we covered there. Don't worry. We're gonna be recapping it in our newsletter as well as a whole lot more. So if you haven't already, go to youreverydayai.com. Sign up.
Jordan Wilson [00:36:55]:
We're gonna be recapping today's episode. I can't wait to relisten to it. I hope you find a ton of value. So thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.