The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:08:58 · Harrison Chase
[00:08:58] S0: I think the reason we need neutral harnesses is that there is a lot of state that you build up either before you build the agent or as part of the agent running. And the state can include things like memory, whether that's short term memory or long term memory. Memory also comes in different form factors. It can be from interacting with the agent versus from the agent learning from the environment. And the reason that this state gets built up is the agent interacts. It goes out. It learns. It chats, and and it learns things to make it better. I think I think one of the most interesting things right now is how to make these agents do better over time and learn. And some of that learning and some of the doing better over time will be in the model weights, but a lot of it is outside the model, and a lot of it is in this in this context. When you build up this context, you really don't want it to be linked to a single kind of, like, model provider. The reason for that is that then it becomes linked there. You can't go to other models. Being able to change models is very offensive and defensive. So it's offensive in the fact that you wanna be able to take advantage of the best model when it comes out no matter who it's coming from. It's defensive in the fact that you don't wanna get locked in, so can't raise rates, things like that. So you wanna be able to switch models. The harness can be very tied to the model. It can also be neutral. The context right now is really intricately linked to the harness. So the harness's job is to basically take the model and interact with all the other things. All the other things includes data and APIs, but also includes memory. And so if you build this memory in a way that is linked to the harness and the harness is linked to the model, then you're kind of stuck.
00:10:23 · Harry Stebbings
[00:10:23] S1: So the true value of the harness structure is the portability of data that it allows the user?
00:10:29 · Harrison Chase
[00:10:29] S0: Yeah. You wanna basically not be locked in to models, and if memory, if context starts building up in a way that is specific to how one model wants to interact with memory or wants to interact with context, that's very bad for portability, and then that puts you at a disadvantage building your your business going forward. We talk with a lot of people about owning their intelligence and what that means, and there's a lot of different versions of that. The version that that that we think, like, owning your intelligence means, we we think there's a few different components of that. But one key component is having kind of, like, this open and controllable agent. Like, you wanna be able to adjust all the different pieces. You wanna be able to adjust the model. You wanna be able to adjust the harness as well because sometimes these model labs are training harnesses to be really good at the things that are in distribution for them, which oftentimes is coding, and coding is great, and there's a ton of use cases around coding, but there are a ton of use cases that aren't coding. And maybe they'll write code as part of it, but they're using that as as a way to do sales, as a way to do research, as a way to do these... The law, as a way to do these other vertical specific things. And so when you get out of distribution of what the the model labs are training on, you wanna have control over, like, what the harness does. And then you also want control over the context as well. So model, the harness itself, and the context make up kind of like this open and controllable agent architecture, and we think that's a big part of what it means to own one intelligence.
00:11:42 · Harry Stebbings, Harrison Chase
[00:11:42] S1: I just wanna back up a little bit. When we think about, like, agent value, so to speak, some people say that the value is actually in the verification, like whether it did well or not, because that unlocks the next step, which [00:11:55] S0: is [00:11:55] S1: automation. If value is derived through verification, who wins? The harness?
00:11:55 · Harry Stebbings, Harrison Chase
[00:11:55] S1: automation. If value is derived through verification, who wins? The harness? [00:12:02] S0: If value is derived through verification, I think the people who win are the domain experts, the people who are able to kind of, like, quantify what that value means, which is very hard. Like, building evals is a very hard job. And so I think a lot of the value will accrue to whoever can kind of write those verifiers, capture that domain knowledge. I'm massively bullish on vertical agents for different domains for this reason, because I think the domain experts who are best suited to write those verifiers or build those evals, as we kind of talk about it, They will be able to build harnesses and models. And it it won't be just the harness. It won't be just the model. It will be some combination of them. So so very practically, if you're getting started building a vertical agent, I would highly suggest you start at the harness layer. You do context engineering. You kind of, like, tweak the harness. And then when you've got users, then you start tweaking the model in post training. I think it's it's much higher ROI to start at the context or harness layer first. And so I think we'll have all these different verticals. They'll be able to quantify what good looks to them in a very domain specific way, and that'll let them build not only, like, vertical agents, but also vertical agent experiences. Different agents might have different experiences. And so I think, like, a lot of the value massively bullish on vertical agents, and so we want to enable a lot of that.
00:13:13 · Harry Stebbings
[00:13:13] S1: You said if I was building that, I would start at the harness layer. We're seeing a lot, whether it's Harvey or I think it's Ramp, build their own models. To what extent are they actually building their own models? Mike Cannonbrooks at Alasdians, don't know, show we really say that it's largely kind of, respectively, BS, and they're fine tuning open source Chinese models. Is that true? And and how do you think about that?