Dharmesh Shah: prediction

Transcript context

…And I think my message for engineers is always like, there's more to go. Like we're still in the early days of figuring out what an agent's stack looks like. Yeah. And I want to push people towards agents with memory. Yeah. Agents with planning. Oh, we have to talk about memory. We got to talk about memory. Let's go. Let's do it. Because I think that's the next, in my mind, the next frontier is actual long-term memory, both for agents and then for agentic networks and a trustable, verifiable, I won't say privacy first, but privacy oriented way. I have an issue with the term privacy first, because a lot of times we say privacy first, when we don't really mean that. Privacy first means I value that above all things. It doesn't matter what we're talking about. And that's just not true, not for any human. Anything that wants to be used. So memory is an interesting thing, right? So the thing I'm working on right now, lots of things in play in agent.ai is around implementation of memory. And there are great projects out there, mem0 being one of them. But the thing that's interesting for me, right, is, and so we see this in ChatGPT and other things right now, where it does have the notion of a longer term memory. You can pull things back into context as needed. The thing I'm fascinated by is cross-agent memory. So if I'm an agent builder right now, it's like, okay, here are the things that I sort of know or I learned from the user in terms of pulling out the, I'll call them knowledge nuggets, for lack of a better term. And that's great. But then when the next agent builder comes out and it's the same user, shouldn't all the things that agent one learned about me, if it's going to be useful for agent two, as long as I opt into it, it's like, yeah, I don't care those things. In fact, I would find it awfully annoying to tell agent two and agent n and agent n plus one, all the same things I've already told it, because it should know, like the system should know. And this is part of the reason why I'm like a believer in these kind of networks of agents and shared state is that that user utility gets created as a result of having shared memory. Not just we should solve the memory problem for an independent agent, but then we should also be able to share that context, share that memory across the system. And that's part of the value prop for agent.ai is like, okay, when you're building, it's like, so we've got, you know, whatever million users and we're going to have growing memory about all of them. So instead of you going off on your own thing and building an agent out as this kind of disconnected node in the universe or whatever, here's the value for building on the network or on the platform, ours or someone else's, because there's more user value that gets created. It's more utility. How do you think about auth for that? Because part of memory is like selective memory. So it takes like scheduling. Yep. I want you to have access. If I have another scheduling agent, you should be able to access the events you're a part of. Yep. And like what times I have available, but it shouldn't tell you about other events on my calendar. Like what's that like?…

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