Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
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John Nay, Norm Ai
Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure...Read more
exploreKeep Exploring
What are some of the key considerations and challenges in building and implementing AI agents for the next wave of technology?add
What type of companies does Norm partner with and why?add
What is the speaker's background and expertise in the intersection of AI and law, and what solution has the speaker been advocating for in terms of AI alignment and democratic buy-in?add
>> Hello, welcome to theCUBE here in our Palo Alto studios. I'm John Furrier, your host of theCUBE. This is a special presentation of theCUBE and the NYSE Wired community, supporting the AI Agent Conference, coming up very shortly in May with all the leaders in tech. Making it happen, making the AI infrastructure happen for agents, enabling this transformation we're seeing that's been hyped up, but agents are here, they are in-market, they are scaling up. And again, it's all about the data. We've got John Nay here, founder and CEO of Norm Ai. John, great to have you on theCUBE and the NYSE Wired presentation and supporting the AI Agent Conference.
John Nay
>> It's great to be here. Yeah, the AI Agent Conference is spot on for what we do at Norm. I'm excited to dig into it.>> Yeah, I mean, what I love about the community that Simon put together is it's really the folks doing the heavy lifting and building the tech that's going to enable in this next wave. And agents gets everyone's attention in the boardroom, but when you get down into the platform, you start looking at who's going to write the code? What's going to happen? A lot of things come up and they're pretty obvious to the mainstream. Compliance, governance, safety, AI going off the rails, so to speak, guardrails, let the ball bounce around. So, agents will be delegated, they will be part of the system. And now, you got to let the chaos rain on the innovation side, but then reining it in is going to be a key thing. You guys are doing a lot of work in this area, so take a minute to talk about what you guys are doing and why you're at the event, and then we can get into some of the core issues.
John Nay
>> Yeah, definitely. So, as you pointed out, the explosion of AI agents and the interest in them, it's great from the innovation and the technology side, but the practical reality of deploying AI agents in a highly-regulated enterprise, which is any enterprise, especially in financial services and healthcare, et cetera, is that they're subject to all the same laws and regulations as a human doing a human's work, but it's even a higher level of scrutiny. Because as we saw with, for example, autonomous vehicles, they're held to a higher level than a human driving the car. And so, that also applies to AI agents more broadly in an enterprise context. So, at Norm, what we're doing is we're working towards the long-term vision of being the regulatory AI infrastructure for AI agents. So, as they're deployed, they're subject to, for example, rules from the SEC and FINRA and many other regulators in the financial services context. And being able to assess the proposed actions or content coming out of an AI agent against the relevant regulations is something that we're working on right now.>> Yeah, first of all, love the background. You're in New York. You mentioned financial services. All enterprise AI right now is really working on some of the lower-in-the-stack details. You're seeing things like AI factories, new semiconductors. I got a couple big stories coming out on some of the new chips coming out. All of that's fueling the next layer of AI agent infrastructure,. You mentioned that. Could you define what that is? Because it's not just business logic, I mean that's in there, but it's actual code, horizontally scalable, but also specialized. I mean SEC regulations, that's not a horizontally-scalable issue, but it's related to everything. It's got to be in everything. So, talk about that piece of what's next above all the semis coming out, all that innovation that's going to just give us super computing power? What does that infrastructure look like? Can you describe and define that for me and scope that?
John Nay
>> Definitely. So, the word infrastructure is very overloaded in this context. So, what we at Norm do, we're abstracted up from the hardware infrastructure, and we're even abstracted up from a lot of the infrastructure around how to deploy large language models at scale and for these use cases. So, where we sit is you have business AI agents that are doing things like customer service support, marketing, et cetera. And then, what we do is we sit across from business AI agents with the infrastructure that is the guardrails, but guardrails 2.0. So, guardrails in language models over the past couple of years has been honestly much simpler things, like, "Oh, don't say a bad word," or, "Don't hallucinate."
The type of guardrails that we do, these are the full on comprehensive laws, regulations, statutes, firm compliance policies, the same type of guardrails that you would see for human employees, we're embedding those guardrails and that as regulatory infrastructure into code. So, what we've done is we've built a system to be able to represent regulatory requirements within computer code, but in a way that turns them into dynamic AI agents on our side. And so, they capture all of the nuances of the different regulations in a way that allows us to, in real time, give it at least a first pass, but a comprehensive compliance assessment. So, that's what we mean when we say infrastructure. We mean the infrastructure from a regulatory and compliance perspective for dynamic real-time guardrails.>> I love how you said abstract, because I think that's what we're seeing in this whole new data layer is abstractions that have, like you said, dynamic value. You mentioned some of the things around infusing that intelligence. Okay, you mentioned the old 1.0. I love that 2.0 guardrails because I think that's where we move from the old chat-bots, you mentioned swearing. Okay, we stopped words on that. No problem, that's just trivial compared to what you're talking about. You're getting into something more specific generative processes that can take into account pre-formulated, but yet, responsive contextual data in real-time, that becomes complicated. Just scope the problem on that. How hard is that to do that? Because my mind explodes around, "Hey, I got to do data prep. I got origination of data, but how do I get the insight? How do I make that work at the right place at the right time?" It's complicated. Share your view on the scope of the complexity.
John Nay
>> Yeah, agreed. It's extremely complicated. And one way to think about it is set aside AI and AI agents at first and just say, "What would it mean to do a compliance assessment against something like an FCC rule and the complexity of that?" And then, now say, "Let's scale that up and do that for AI agents." So, it is even more complex. And the way that we've done this is we've said, "Let's put a significant amount of upfront effort into understanding those regulations and of how you would represent them in an interpretable computer code format, and that alone has forced us to build a lot. It's forced us to build a no-code platform for lawyers to be able to put their domain expertise into the system. And then, to your point on the contextual actual analysis, it's also forced us, when we're thinking about deploying this to do a lot of workflow work, so to be able to say, "Does our regulatory agent have the right context of the type of analysis that needs to be done, the relevant data where this thing is going to be sent, if it's, say, for example, some content that's being reviewed against a regulation?" And so, we've had to pair together enterprise-wide workflow software along with the regulatory AI agents to be able to give the AI agents enough context to do the analysis, and that's something that we frankly didn't expect to set out to build. We are an AI company, we're a bunch of AI engineers, but we then had to do that to get these enterprise-wide deployments and give the systems that context.>> Yeah, I'm really psyched that theCUBE has a new studio at the NYSC because a lot of startups that are, frankly, just coding away are in an area where there's a ton of customers. You go four blocks and you can have four great customers. You mentioned regulated industries. And back in the old days, they were not fast-movers and they had complex systems and they were really locked down. I mean, that's my word, but I mean locked down in the sense of they weren't into the whole shiny new toy kind of vibe, but AI now gives them a different view. And I want to get your thoughts on this, because regulated industries have been doing the blocking and tackling of domain expertise, data, management, they have done all the work. Can you share your thoughts on that perspective? Because now we're seeing a surge of regulated industries stepping in with AI and getting massive lift because they've done the heavy lifting for the other reason, which is compliance and reporting. Talk about that dynamic and what it means for them as they look at this opportunity. If I'm in a regulated industry, whether it's financial services or say biomedical or healthcare, there's tons of these environments that have been set up for success where they don't even know it.
John Nay
>> I love that point, John. That's honestly such a good point, that they've already laid so much groundwork. They've put together playbooks, they've put together stringent compliance procedures and policies. And so, they have a lot of the raw material, these large, highly regulated institutions. And so, what we do at Norm is we partner with them very closely. We work with, basically, only large enterprises, and we work with their chief compliance officers. We also work with their chief technology officers, their key stakeholder, and we take a lot of the raw material they have, and we turn that into not just static playbooks that they have now, but these dynamic playbooks in the form of AI agents. But the fact that they've already done that work and they're already in the mindset of having these guardrails around this is actually why we chose to only work with the most highly-regulated companies that take this extremely seriously. And actually, yesterday we just did a public announcement that we brought on the head of compliance at Blackstone, at TIA, at New York Life, at COTU. And then, we also brought on the CTO, or equivalent, at those same firms, including Vanguard as well on the technology side. So, these huge institutions that some of them have been around for more than 100 years, they're taking it that seriously that they're joining our AI Agent Advisory Committee, they're joining our Regulatory Advisory Board, and they're really leaning in to help set the norms for how to do this.>> Well, it's great to drop some news 10 minutes in. That's awesome news, we'll make sure we capture that. But let's get into the news piece there because what's been the reaction from them? Because I got to think that they're licking their chops, their eyes pop open. Because if you're in that world, you are kind of... I won't say blocked out of the horizontal scalability to say cloud computing. I mean, the SaaS market grew on that. We saw that, clearly. But now, they're in this world where... Are they getting this? Are they looking at this? And how are they approaching it? What are some of the sequence of events that goes on when someone from Blackstone comes in and says, "Look, you're sitting on the gold mine here." Because of the vertical specialization combined with the scale on the horizontal, you have a perfect storm. What's their reaction? What's the vibe? What are some of the conversations? Take us through those sequences.
John Nay
>> Yeah, so part of what we do is we both work on the more traditional compliance processes and say, "How can we help out making those more efficient and effective today?" But then, along the way, we're future-proofing organizations to be able to have those dynamic guardrails. And right now, most of the content, most of the decisions and actions inside companies is still human-produced. So, by getting in there though, and laying the groundwork for the infrastructure for more prosaic use cases today, we're then helping them to future-proof towards the AI agent world. So, then what we're seeing at some of our clients is that month over month, more and more of the things that are being produced that are subject to compliance review are coming from AI. So, we're still at the early innings of that, but we're seeing a pretty consistent trend. And so, the compliance teams, the way they look at us is that they're seeing those trends too. And they're saying, "Well, how are we going to handle the potential flood of that? So, if the AI agents on the business side are infinitely scalable, roughly infinite, then from a legal and compliance perspective, how are they going to review all of that? How would they ever keep up with that tsunami of content that needs to be reviewed? And so, they're viewing us as that, as about, "Okay, let's lay the groundwork and let's be in a position to be able to handle that going forward.">> That's awesome. And one of the things I want to get your thoughts on while I have you here is that I know in the conference is going to be AI Agent Conference, we'll have a lot of technical conversations. But we're seeing a generational shift in how tech is built. AI safety, AI laws are being built. People want to protect the range of AI, if you will, but to keep the human intelligence and the human in the loop front and center. I'm an old Terminator fan, so I love the whole Skynet analogy, but you're seeing a influence around engineering research and policy coming together at once. At Google, I talked to some of the folks over there, their AI research is fundamental into their products. So, can you talk about the intersection of the engineering side of it, the research you guys do, and then the policy impact and how you guys think about those three things? Do you think about them together? Because that's where the action is. You can't just do policy in a silo. You can't say, "Hey, we're going to make some laws." And, "Oh, by the way, I don't have a computer science degree. I went to law school." Great, but... Take us through the thoughts there, because I think this is a cultural shift. What's your reaction to that?
John Nay
>> I'm really excited about it because... So, for me personally, just real quick, so I did a PhD focused on more of the technical side of things. So, my dissertation was around how to get machine learning models to better understand social and economic and legal and policy processes. So, I was more on the technical side, and I still am, but I've always been drawn to the policy side of things. And I moved here to New York originally to come to NYU, and I ended up being an adjunct professor of law at NYU Law School. So, I've been at this intersection of AI and law for more than 12 years now. So, for me, it is great to see that appreciation of the confluence of those factors. And I totally agree that it's completely necessary, because now that technology is moving into this more agentic and autonomous nature, then it's going to just automatically implicate society at a very broad level, and then therefore, policy and law. And to your point on, AI alignment and how people are thinking about that, I think there honestly has been a blind spot in that community and in that world where it's been so technically-focused on how do we think about this from a purely mathematical or computer science perspective? But as it gets deployed, the real reality of this is that this is going to be impactful for every single citizen in the United States. And if there isn't some democratic buy-in for the rollout of this, then it's not going to go well. And there's going to be a lot of chaos and there's going to be a lot of pushback. And so, what I've been advocating for for about a year or so now is this idea that the only democratically-determined database of AI alignment data is democratically-determined law and that's our source. So, let's figure out a way to turn that law and policy into something that AI agents can recognize and abide by. So, I think that's the way to tie it all together.>> I love that. I think that opens up a conversation, maybe we'll pick it up later on, around curated data sets as models. Smaller might be better than big. Vertical versus horizontal models can be more acute, higher fidelity, better outcomes, better enablement. I want to get one more thing from you on this policy thing, because this comes up a lot on the idea of changing the narrative in tech. And I think your point about intersecting those three things brings up that democratic version of AI in the product side. So, if you had to give advice to folks making laws right now, what would you say to them? What would be your narrative on, "Hey, this is a direction to take. It might not be fully baked in terms of visibility, but directionally correct, this is the path we need to go down."
What would you say to that? Because this is the AI for good conversation comes back down to what laws are in place. I mean, clearly the business imperative is there, because if it doesn't work, there's no value. So, it's like a impact meets profit objective. What would be your advice to lawmakers and folks really cranking the brain cycles around what is the best approach?
John Nay
>> Yeah, so my main topic for lawmakers and regulators is around how they can use AI themselves. And then, I think as they grapple with that and they think about that, then everyone's in a position to be speaking the same language and be working on building the bridges between corporates and regulators. And so, Troy Paredes and I, he's a former FCC commissioner that we work very closely with at Norm, we published a paper a couple of weeks ago titled How Regulators Can Use AI, and this was in Vanderbilt Law Review. And we laid out a few ideas for how regulators themselves can think about using AI for facilitating permitting, for putting together ways to analyze regulations before they're finalized and look at an impact analysis. So, that would be my main high level thing is think about it from the perspective of yourself and the government and as a lawmaker and as a policymaker. And then, how can you leverage it? And then I think that's the best way to start to wrap your heads around. Then, now let's have the conversation between regulated entities, regulators, and lawmakers to work together and build those bridges.>> That's awesome, John. It's ironic too. I know that Simon and the team are doing the conference AI Agent Conference in May. It's a lot of Salesforce alumni, second multi-time founders. Marc Benioff was on theCUBE yesterday here in Palo Alto, and he actually mentioned that point about use and his success is about rollouts that are user-based. Use it first and then go full scale. I mean, that's the playbook in software in general, but you get a test case. So, that advice is awesome. Just get your arms around it. Don't try to do it on paper, use it and then look at the broader impact and sequence to a bigger position and then go from there. You agree with that?
John Nay
>> I agree with that. And we're fortunate to count Mark as an investor in Norm Ai as well. So, no, I totally agree with that, and I think that there's no better way than just doing it. Just doing it yourself and learning and using it, that's the only way to really have a more productive conversation.>> Awesome. John, great to have you on. I appreciate your time again. Let's follow up. A lot of strings we can pull on this. So, congratulations on the great venture. I didn't know Marc was an investor, that's serendipitous, but great. Quick last question, 30 seconds. What's the event about? Share your thoughts on the AI Agent Conference that Simon and the community's putting together. What's it about? What's the focus? Who's attending? Put a plug in for the event.
John Nay
>> Yeah, so I think what's so great about this and unique about this is that, well, number one, it's in New York, and as you can see from my background, we love New York here at Norm. And then, number two, it's bringing together both the technologists, so there's a lot of people attending and speaking that are at some of the large banks and other firms here, but on more of the CTO side of the house. It's bringing that together with the compliance angle, for example with Norm, and with a lot of the other perspectives that are critical for AI agents to be deployed in large enterprises. So, I think this multidisciplinary nature of it and the fact that it's here and there's a lot of large financial services, institutions attending is going to make it a great event.>> Well, Brian Baumann and myself at theCUBE, NYC Wired are really psyched to be part of promoting and highlighting the success you guys are doing. Again, looking forward to seeing you in New York soon.
John Nay
>> Yeah, looking forward to it. Thank you so much for having me today.>> Cool. John Nay, founder and CEO of Norm Ai. Again, this is the convergence of technology, engineering, research in AI. And also, impact on policy, democratizing the AI component is super critical in the business imperative, but also to society. Again, this is where we're seeing a cultural shift and continuing to be led by tech for good if it all works out. So, theCUBE's got you covered, theCUBE and the NYC Wired. I'm John Furrier, your host of theCUBE. Thanks for watching.
