The Decentralized Agent Economy: AI, Privacy & Identity

Hello, welcome everyone. Thank you. Hi, hi. Let's face today, I think. Hi, Nova. Hi. Hi, everyone. Hello, sir. Yeah. Today we are joined with Nova, who is the business developer for AI. We are also here with Chris Blanco. CSOa Buildex. It's great to have you here with us today, Chris. Ah, thank you. It's great to be here with you and with Nova. Absolutely. Great. So before we start, I just want to say, I, I pour my ideas into AI and I let it help me humanize them. So this topic we are talking about today, AI and privacy. It's personal to me. We are going to be looking at something that's moving very quickly. The rise of AI agents and infrastructure, they are going to need as they start doing more than simply answering problems. So for AI is approaching this from the decentralized AI and agents marketplace side, while Bell Dex has been building privacy infrastructure across communication, identity and payments. So rather than talking about AI or privacy in isolation, what we're trying to do today is to explore what happens when these worlds begins converge. So Nova, let's start with you today. Anyone discovering for AI for the first time? Could you please tell us what is for AI building? And what problem are you trying to solve in the AI agents ecosystem? Yeah. So far, anyone having a world as in the first time like a forum is basically decentralized marketplace on agents. The core problem we are sorry is right now if you want to AI agent build something specifically, there is no real open space to request that any find a builders or have that agent actually go and work with other agent. So we build their three pieces around that say request where you search and publish their work what they need and what you need to build and agent of way developer, develop and like whenever we get to the request and then we have agent up there where developer develops that agent and publish on our side. And last one is the agent space where multiple agents can actually coordinate one more space complex. So it's just to about another child board or more about them making the agent capability something people can request build and use the open economy. Yeah. Thank you Nova. So this is Nova explaining these spaces that exist in for AI how you can request amputed. And this is the agent ecosystem side of the conversation. Chris, build X comes at this from a different starting point. Yes, you spent years building privacy infrastructure and build X itself has also for AI agents are increasingly becoming parts of that vision. Build X started with with privacy infrastructure and we are now looking increasingly at AI agents. So I just want to ask why do you see AI as a natural extension of what's build X has been building for years now? Yeah, absolutely. And if I may, let me introduce myself to today to the audience. I guess some of them, you know, might be appear here and they may not have met maybe four. As you said, I, you know, the chief strategy office officer at Beldex and this is very important for me because my background comes from work three for identity. And for the last couple of years, you know, working extensively in AI. So as you said, you know, this is conversion and this is a great moment. So I just want that opportunity to spend to people a little more coming from. And then going into your question. I think that the one line there would be like because AI doesn't produce the need for privacy, you know, it multiplies. And as you said, you know, Beldex started with one question. How do you use internet without, you know, looking your whole life? And, you know, we started with the private payments. Private message. This applies VPN browser private names. And so we just, you know, we're covering the whole data life spectrum. And now, you know, if we look on what's next or right, what's next, what's actually happening now, it's agents. You know, agents there actually, you know, the message people pay, the blue brows, you know, the whole identities on our behalf. And that's exactly what we've been spending years building for people. And now, you know, those same layers are going to be devised by, you know, the agents. So for us, it's like, people, it's like a natural evolution, you know, like, we build infrastructure from privacy. And now we just have a new set of users, which in these cases are the agents. And our problem is the same. So, you know, we're going, you know, we are privacy infrastructure for Web 3. And an AI because in the end, we have like privacy infrastructure for digital life. The live area. Yes, absolutely. This is Chris talking about privacy. And let's go back to for AI. And hearing from what Nova experienced with us, he was talking about the requests, agents, hope and agents space. So I just want Nova to explain these species together. How do they connect like from someone needing an AI solution to agents. Actually, working together. How do these pieces connect? Nice question, don't mention. So, shoot, so picture like this, like someone, however, has a problem. They do not know how to build the agent themselves. So they publish a request other are describing what they need. Then builders on the platform, like our developers and see that demand. Like we see the demand if that the request is really available. And we need to build that is a word. Then go to build agent for it. So once again, it's build it lives on our agent, like in our in our platform. That's basically described layer other users can find it to use it. And the builders on for that they using. So now if the task is bigger than one agent can handle their where agent space like if your task is big. So one agent cannot ever to handle your all task. Then you can go to the agent space and comes in the multiple specified agents working together towards in one object inside of the forcing single agent to do everything. So all real goes to demand and build and discover and coordinate. Yes, this this air agents which are being built. It brings us to an interesting question. Like the more copy this agents become the more they can potentially act on behalf of humans, users rather than simply just respond to them. So back to Chris now. Presumably it's changes. It changes the privacy conversation as well. Yes, now let's picture it's like an agent who is trying to book your flight. Now it sees your passport. It sees your your card, your travel dates. Now when we talk about AI agents becoming more autonomous. What's new privacy problems are paid that we do not necessarily have with you know the normal applications, which are very important. And the most important point I think because people until now they've been focusing on privacy of what they're doing, you know, keeping track of where they post certain things or you know how they all the behaviors. Themselves, you know, the actual user being responsible for what information goes where some of them they know some of them they know. But now we bring agents we we have definitely new privacy problems, you know, I can take you off of your ones. The first one I would say context, you know, and that you know if you use a normal app to just they just know what you type into it, you know what you're actually. And then deliberately typing into the app that's all the contests they have of you, but when we talking about agents and they know any document that you put into it, you know, let me know your your finances, your credentials. And maybe even years of other information about personal details or how you work. And they keep that context and they keep that and they think that context everywhere they go and operate so that will be the first. The other problem would be the second one, which is like the agent stop into other agents because you know as a user you don't know what's actually happening or where it's actually happening. So everyone of those conversations is in a place where you have visibility of what's going on in there and where they can be data leaks. And the third one I would say is metadata, you know, even if the content is encrypted who talks to whom and when tells the story and they can still be a lot more fun. So let's say like company agent keeps talking to a legal agent and you know MLA agent. You don't need to read the entire messages were really getting for what's actually happening there. So just the metadata itself is actually quite affordable. And then I mean the most obvious one is like as you said, you know, like payment, you know, they're going to be 100 payments on our behalf. And so if it happens on a transfer change every every payment and agent make is public. You know, they can build a map around the spending and map out your strategy and what evolve from home, what time, you know, they can definitely build a profile there. And all of that happens without us without the user actually being you know fully aware of it and it's happening in machine speak, you know, nobody's actually watching every single step. So they would say that if we go into some our eyes all of these the next big privacy problem is not protecting people from machine is protecting what machines know about people and how they use that information not for an experienced act but more in how they actually treat that information, you know, what's leaking what's not leaking was being shared, you know, sort of like our rails have been in place. And to use that information is the most private secure way. Yes, thank you Chris.