Nate B. Jones - AI News & Updates: Agents Inside and Outside the Enterprise

Yes. I can also talk loud. Well, it's great to be with all of you. I feel like I'm more excited to see what you're building than anything One. And so I'm very excited for the rest of the talks that are gonna come after me. But in terms of the news and updates that that came to mind as I was asked to sort of talk a little bit about what's going One, I think I wanna talk about this world that we're all building for that exists both inside and outside the enterprise or inside and outside the startup of the System. And that rules the world of agents. Right? We talk about agents all the time. But I think that there are different dynamics when we are, building for the market versus when we are building internally for our own tooling. And I wanna just take, you know, 10 minutes, explore that a little bit, kick it around a little bit. Maybe we can have 1 or 2 questions, and and get going. So we'll we'll start with our customers first. I'm former Amazonian, customer obsession, sort of got thrilled into us. If you're building for the Internet now, the the way I like to put it is the Internet was the attention economy for a long time since, I don't know, 2000 since Google. How do you get attention? How do you keep human attention? How do you drive attention through the funnel? I, you know, I was in marketing for a while. I built Partner tools. And it was always about how you connect with a person who's making a buying decision, whether they're buying a pair of Nike shoes or, you know, whether they're making an account decision for a sax. And you need to connect with them 1 way or the other. And the products you build have to be for that human world. And the thing that's changing underneath all that, like, we talk about Agent. But if you think about the underlying dynamics that are shifting is that they're not shifting from an Intelligence economy to an interpretation economy, where you have to assume that whatever you want to Agent l m before it ever gets done. And so a lot of the example. One like One make things concrete. Offers genuine signal. And I'll give you I'll I'll give you an example. I like to make things concrete. So, I had, until very recently, an ancient sound System, and it was terrible. The wires were not good. My receiver wasn't good. I was limping along on it. And I finally decided I'm gonna bite the bullet. I'm gonna buy a new sound system. I did not go to the Internet into Google and just say, hey, can I have a sound system? Please show me my options. That was not the the the ad selling exercise that Sergei wanted me to do. I did not do that. I went instead to my L. O. M. And I said, hey, and I tried this in both quad and Chad Cipiche. I said, hey, I One a sound system One I want you to give me options. And so already I'm filtering the internet through. I never looked at a web page until I clicked by One after CLI after stretch, a genetic sessions, I don't even know that I have to do that anymore. And so I went through I went through a process for over a week where I actually gave it the dimensions of the room. We talked about speaker placement. We talked about budget. We talked about what kind of wires I would need to get. All of that happened inside the l m out of view of the marketer, out of view of anybody else. And it was dependent on the product availability being something that the that the l l m could filter through One that the l l m could actually pass signal on. Now I had no idea if I actually had what I would describe as the best consideration set. I don't know. Right? I just know that I'm in the habit of using an LLM, and I'm gonna use the LLM to do my purchase Intelligence, and the LLM is gonna provide me a consideration set. And so if you're building for that world, the question then becomes, how do you present product in a way that allows the product information to map reliably to user intent? I'll give you another example. I'm a former coffee person. I love coffee, and this is a wonderfully vague coffee request that I think you could meet with an agent that illustrates the data problem we face as builders. I want authentic coffee. Now One does authentic mean? Right? It could be anything to anybody. But an agent who is working with you, who understands your history knows that when when I say authentic coffee, what I mean is it naturally processed Real Ethiopia that is sort of lightly roasted. I wanna roast it within the last 2 weeks One I want it on my door in 2 days. I have lots of requirements. And the agent knows me well enough that it knows that I need that. And so it's going to go and interpret that it's gonna go and look for that product. And if the product metadata is there, then it's gonna be able to find that and include that in the interpretive center. And I'll go farther. If you have an agent who doesn't have that background with you, you don't talk about coffee with your agent. You have much more important things to build with the agent. The agent then has to go out and infer and map the meaning of your intent. And the way it does that is by looking to see what products Sn the competitive set actually offer that kind of mapping that maps and says, this is what authentic means. Right? This is the this is the authentic interpretation we have for coffee. And it's like, oh, thank god. Like, someone finally helps me, the agent, understand what's going on here. And that is like a tiny micro consumer example, but that's also what's happening if you talk to people who are marketing for enterprise right now. If they're marketing SaaS right now, if you're marketing tools right now, so much of it is getting compressed through the the interpretation layer that we're all bolting One to our experience of the Internet. Google even does this. Right? You go to Google. Are you leaving the Google search results page? Are you reading the Google answer and say, yeah. Good enough. I'm moving on. And and this is big enough that it's affecting organic traffic to sites because people are reading the AI summaries that Google provides. And so increasingly, the interpreted Internet is becoming the way we experience the digital world, the way we buy digital products and services. And so we are building what that implies to me is that we need to be more opinionated. Because if you're not more opinionated about your software, you're going to get flattened into the Internet average for your category. You're going to get sort of compressed into the middle part of the bell curve and it's gonna be like, well, you know, 1 of 3 other SAS tools in this particular category and, you know, have fun with that. Right? One of, you know, 18,000,000 AI tools in this category have known that. And there's no opinions there. So that's the outside look. That's the customer obsession. Look, that's where I think we are going and what we need to think about when we build like at every right when we're building. If we come inside the firm for a minute, if we look at agents inside the firm, a lot of what I see is people obsessing over what I call sort of fancy chains of meaning. Right? Like, they're thinking about so I put the element here and I put the memory here and then I put my tool registry over One. And this is my cool, like, stack of LEGO bricks that allows me to build a really neat internal dev tool or a really neat tool that's a pipeline for marketing materials or whatever we're building inside. But I think at root, 1 of the things that I see that's missing is we are missing the understanding that the agents that we are working with need to be treated as if they are growing up CLI, and the harnesses we are putting around them are very modular and very at least need to be ready to be very temporary. Boris Churney talked about this. He had gave a talk on Klaus Co, I think, earlier this Works, and he said they effectively have to rebuild their harness at Anthropic for every model because the models have different strengths and weaknesses and model capabilities scale. And that got me thinking, how often are we thinking about not just modularity and what we put out for customers, but enough modularity internally that when we have the next decimal point model drop, instead of saying, oh, gosh. What do we do? We say, let's swap in and assume that we don't have to have the Partner component of our workflow anymore because the new model seems really good at it. We'll just drop it out. It shouldn't take too long, and we'll be back at it in a regular pipeline. And I think about that a lot because I think that a lot of the future of how we build is basically enabling constructor sets internally that One the door to a wider range of inputs to the build process as long as we maintain good code hygiene standards, as long as we are guarding the value of the architecture that we are putting together. Like, I was talking to a designer, I think, yesterday. And and we were talking about Managing, and he lost his job and sort of how he changes and all of that. And 1 of the things I called out is he was like, I'm not technical. I'm not gonna learn to code. And I said increasingly does not Azure. And it doesn't matter because you're gonna buy code. That's not the point anymore. It's 2026. It doesn't matter because your engineers should be setting up a pipeline that allows you to submit clear intent, drive the design polish you want as long as the code you're writing passes their emails. And you should have a patient that is driving to pass those emails. And then you don't have to know if it works because the emails are so good. And your job is basically to put the design polish into the email Sn that's really clean. And the engineer's job is to make sure the non functional requirements aren't shit. Pardon my French. And so often I see less attention on effectively setting up our modular pipeline so that it it enables us to build for agents that are getting smarter System. And so I want you to think about the idea that by Christmas time, your agents are gonna have 5 or 10 x more blast radius inside the enterprise. And you have to build your pipelines now so that you're thinking about that modularly One you can extend that surface area. And I think that's something that we get so wrapped up in what the tools can do One. Super cool. But if we're responsible, we have to think about where they're gonna go and how we build now so that we suffer less come November, come December when, you know, Chad g p t 6 drops or whatever number it is drops. And suddenly, everyone's breathing down our necks saying, well, you gotta build for that. Right? You know, you make sure you have this thing done by whatever. Build modularly, and we're not gonna have that issue in the same way. And I know that that's easy to say, and I know that it is hard to do. And I know it's increasingly hard to do because when we built modularly for developers before, it was building modularly for everyone inside the engineering team. And now when we're building modularly like Stripe, Stripe, this got kind of covered up, but Stripe built a platform that ties their designers and product managers into the build flow, and it's more visual. It's less sort of in the terminal. And they took the time to do that to wrap more people in. And so you have to think across many more teams as you're putting these pipelines together. So I get that it's harder, but I think that's where we're going. So that's my thought.