Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Angela Jiang 0:00 The last layer of abstraction on top of this is probably the coordination layer. So you have knowledge, and you have execution, and you have coordination. And at the coordination layer, we're beginning to think of these things called, like, strategies, where basically it's almost like a meta harness. The true low-level harness is designed for execution, but the next one is about, okay, if tokens aren't really fungible, and you need to give them different jobs, like maybe so... this token is advising versus this token is executing, you want to start composing these, like, these kind of orchestrated strategies that go together, and they should sit on top of all these things, because at the end of the day, you still need to execute, and the execution still needs to know what to do. So everything, in theory, should kind of, like, ladder together. And so I think, you know, if you were to look at our roadmap and the... maybe kind of project forward a little bit where you kind of expect this to go, we'll move more and more, uh, from the knowledge layer to the execution layer, from the execution layer to the kind of coordination layer, in terms of the abstractions that you can see us put out.

Sonya Huang 0:47 [upbeat music] Katelyn and Angela, thank you so much for joining us today. Lauren and I are thrilled to have you here. You are responsible for building Anthropic's platform, and so you are responsible for building what I think is one of the most important, if not the most important, developer platform in the world. And we are really excited to interview you today to understand more about what's ahead. And so maybe just to get started, can you give us the context of, you know, what is Anthropic Platform and where do you sit within Anthropic?

Katelyn Lesse 1:28 Yeah. So Platform is both our externally facing APIs, our developer platform that people build on top of when they want to build applications and system that... systems that access Claude's intelligence, as well as internally, um, we run our product infrastructure, and basically we're the layer that our apps build on top of, uh, internally as well.

Sonya Huang 1:49 Awesome. What's your North Star as a team?

Angela Jiang 1:51 It's a great question. We actually, because we have both internal and external, we actually kind of have, like, two North Stars, which is probably, like, you know, you'd be like, "What? There should only be one North Star." But, um-

Sonya Huang 1:59 [laughs]

Angela Jiang 2:00 No, we-

Sonya Huang 2:00 We're a tri-planetary system.

Angela Jiang 2:02 Yes, exactly. Ex- they're separate solar systems, so it's fine. Um, but, uh, on the internal side, like, we really want to provide is, like, literally as much leverage as possible for our internal teams to be able to ship, like, AGI-pilled products. Um, and we want them to be able to move fast, be able to have reliable, like, great, like, uh, platform, uh, to be able to build on top of. But I think the key bit about speed is, like, really intentional for us, and we really, really care about that internally.

Angela Jiang 2:27 Externally, um, we a- actually have a lot more, like, complicated set of things. Um, but one of the true Norths that we have there is to be able to basically give any builder the tools to be able to work with Claude to build whatever they want to build. And so it's a bit of a broad statement, but as a result, that, uh, boils its da- itself down into, you know, being wherever that business is. Like, we really care about, like, bringing our platform really, really close to that business. This is why we spend a lot of time with the hyperscalers, integrating really closely, uh, directly with them, like AWS, Google, so on and so forth. Um, and it is a lot of, like, h- primitives that we end up creating. We want people to be able to express what they think their product should be. We want them to be able to almost do, like, custom software in their own way. You know, like, in this new world with AI, uh, what used to be probably economically impossible was that last mile of, of custom software now, in theory, should be, like, very, very achievable. Um, and we want to give them all the tools and all the capabilities to go and do that. And so sometimes that comes in a form of primitives and APIs and higher order abstractions, and sometimes that comes in the form of just, like, standards. Uh, so for example, like Skills and MCP, um, those are things just, like, Claude needs them to be useful. And we can just give them out to the rest of the ecosystem, work with everyone to help you create those things and get the best out of Claude. So, um, I would say externally, you know, we really are oriented around just helping you just be able to build, but internally, that orientation, while still existing, is probably more, you know, sp- specified towards speed and being able to move really

Angela Jiang 3:54 quickly. Yeah.

Lauren Reeder 3:54 How do you decide what goes into the platform, what gets externalized and what doesn't, to decide what products should be available?

Angela Jiang 4:02 Yeah, I mean, we generally try to have a philosophy that we try to be consistent across the board. It's actually one of the reasons, uh, why we do internal and external. Um, there's plenty of other, you know, platform businesses and constructs where you actually, like, bifurcate these two things. Um, for us, we kind of try to intentionally keep it equal, and then as a result, we try to hold this philosophy as much as we can around, like, you know, for any builder, internal or external, even though for our internal builders might have some s- slightly different requirements in the same way any user would have slightly different requirements, uh, we want to have the same primitives that are available to everyone. And one of the, maybe the overarching thesis for that is that we've just seen, like, the capabilities of these models just grow in such just exponential, and it's really hard to figure out, like, a long-lasting form factor. I think two years ago we were all like, "Everything's chat." And now everyone's like, "Forget chat." And it's just like agents. And, like, there's gonna be another form factor, another form factor, um, and we kind of imagine that, like, constantly evolving. And so the best way for us to kind of enable that for everyone, and also ourselves, is to actually build a really robust platform that gives people those kinds of, like, tools to figure out what those form factors are. And I don't think we, by any means, feel like we're the only ones capable of figuring out that form factor, like, not at all. In fact, the more democratization we can do on that and help people and allow people to experiment, I think the, the more those form factors will actually kind of naturally come out of the market.

Katelyn Lesse 5:15 Yeah, and I think within our team, we've, we've had moments where we're experimenting even with just, like, a packaging up of our primitives in a different sort of higher order way, and we've thought about, "Okay, cool. We've solved this exact type of problem with this product that we've built into the world, and so we can go and dog food it for ourselves," but we'd never want to fall into this trap of, like, we're over-indexed on the problem as it needs to be solved for an internal user. Like, because exactly what Angela said, internal users have very specific requirements, external users have very specific requirements. And so if you over-index on one or the other, you fall into a trap. So a lot of the time what we'll do is dog food something internally at the same time that we open up early access of some sort with external customers, so that we can kind of get a range of feedback and bring those things back into the platform.

Sonya Huang 6:00 I'd love to talk about the higher levels of abstraction that you discussed. So I guess at the base level, this is just, you know, raw access to- Claude Opus or whatever tokens. How do you think about the, I guess, the layer cake of abstractions above that?

Katelyn Lesse 6:14 Yeah, if you look back, um, so when I joined Anthropic around a year ago, um, the platform was basically just the messages API. It was a messages API, um, you know, we had come out with standards like MCP. We obviously have developer tooling around our SDKs and our docs and our console and things like this. But for the most part, it was a stateless API. Um, and what's interesting to Angela's point on form factors evolving over time is we found a lot of our customers solving the same problems over and over again that we also were solving over and over again around as the models got better at running for longer and working with more context at a given time, you want to build agents that can succeed in a kind of long-running context and even a remote context that doesn't necessarily have a human in the loop. And so we found that we could piece together our primitives and stand up all the same infrastructure that we're finding ourselves standing up internally to power our own products, and arrive at some higher order abstractions that let you do more agentic work out of the box. And the problems that we're solving for you are, you know, infrastructure being kind of a hard thing to deal with. Like, how do you figure out spawning sandboxes that are going to have the right governance and security and like, you know, spin them up and spin them down when you need to, or the storage around transcript sessions so that you can resume a session if you stop it and pick it back up later. Um, so that infrastructure is a big thing that we wanted to be able to provide more of out of the box, and we do more of that today. Um, and then the second thing just being harnesses and harness engineering. There's a lot of thought and energy going into how do I do my prompt caching and how do I manage my context window, as well as how do I actually just get more intelligence out of the model, um, and how do I manage my costs and things like that. So we've kind of packaged up our primitives a bit more in tune with the problems that we found ourselves solving to provide more of these things out of the box for people so that they can, if they're building systems for them- themselves internally, if they're building products, they can just be more focused on the problems that they want to be solving. And if they want to offload some aspects of those problems to us, they can. Um, and that's kind

Katelyn Lesse 8:19 of the ethos.

Sonya Huang 8:19 And are your customers generally choosing to opt from the grab bag of stuff that you offer, or are they... Like how, how often are they opting into the just the, the managed agents offering, I guess, just take care of it all for me?

Angela Jiang 8:29 Um, it varies by like the, the user group. So like for, I would say, you know, like really AI native startups, like the ones who are like tinkering and like experimenting at a really low layer, they're just going to go for the primitives. Um, and then for everyone else, uh, these kind of classic like more like enterprises or areas where it's like the purpose of the startup or the philosophy behind the startup isn't necessarily to optimize on, um, some kind of hill climbing piece. It's more like stringing together a bunch of workflows and, you know, providing unique user value at, uh, to that user. For those people, um, you know, it's just kind of not their core competency. It's not where they want to focus their time and resources, and they reach much more for these kind of like higher order like package offerings.

Sonya Huang 9:07 What are some examples of the primitives you've released at different layers in the last few months? We've seen a few of them. Would love to hear.

Angela Jiang 9:13 Yeah. I think maybe one framing I would give, um, for some of the constructs that Katelyn was talking about is like, and this is a bit of an oversimplification, but effectively, there's approximately like three like layers of this cake. At the very bottom is just kind of like, like knowledge. And so at this layer, like in many ways, it's, it's knowledge about the model, it's knowledge about the things that the model needs, and it's just like the ability to know how to actually do something with Claude is maybe the way I'd phrase that. And so there, the primitives that we have spent more and more time on, uh, have been actually things of the past, 'cause like we still evolve them, but they tend to be a little bit more baked. Like for example, there's very specific shapes and parameters we put on the messages API, and it's more like trying to expressly like, uh, showcase Claude's like design, like Claude the model's, uh, actual design, the way it thinks, the way it respects certain parameters, the way it kind of like, um, will do tool calls, like all of those different pieces. And then we started standardizing like tools, and then we started standardizing bits and pieces of like context that you could put in at different moments in time, which is concretely like skills and like memory. And so those are like the kind of like knowledge layer type of abstractions that we've put out over the past, um, I guess like year plus, plus a bit.

Angela Jiang 10:22 Um, the next layer of abstraction that we've actually started to spend more and more of our time on is like once you kind of know stuff, you then need to like execute. And so at the execution layer, that level of abstraction is the part that Katelyn was talking about around like we're doing these like higher order pieces, but like what are we putting higher order there? It really is because you're now getting Claude to execute work. It's not just to know something, right? I can give it a question, it can give me an answer. You can put string a lot of that stuff together. Uh, but now if you need to execute, like do work, give me the output, edit files in a bunch of different systems, that becomes a lot more complicated and requires infrastructure to handle. And so that layer is basically, I would say, a low level harness plus managed infrastructure as like the set of abstractions. Today, we just like our high level product for that is called Claude Managed Agents. Um, and so that's like a piece, but we started to wrap more and more pieces in that.

Angela Jiang 11:09 Um, I think there's gonna be a layer like on top of that. We have like some inklings of it we started to build towards, but the last layer of abstraction on top of this is probably the coordination layer. So you have knowledge, and you have execution, and you have coordination. And at the coordination layer, um, we've started to expose some of these in ways that like aren't very obvious. But we're beginning to think of these things called like strategies, where basically it's almost like a meta harness, where the harness, the true low level harness is designed for execution, but the next one is about, okay, if tokens aren't really fungible and you need to give them different jobs, like maybe so this token is advising versus this token is executing, this token is dreaming versus this token's executing, so on and so forth, you want to start composing these like these kind of orchestrated strategies that go together, and they should sit on top of all these things because at the end of the day, you still need to execute, and the execution still needs to know what to do. So everything in theory should kind of like lad- ladder together. And so I think, you know, if you were to look at our roadmap and the maybe kind of project forward a little bit where you kind of expect us to go, we'll move more and more, uh, from the knowledge layer to the execution layer, and from the execution layer to the kind of coordination layer in terms of the abstractions that you can see us put out.

Sonya Huang 12:12 That's a really cool framing

Katelyn Lesse 12:13 Very cool. How do you think this all comes together into a broader ecosystem beyond just the things that you guys are building? How do you help support people building products on top of it, and how do you help them get the most out of all these pieces?

Angela Jiang 12:25 Yeah, I think this is, like, super top of mind for us. Like, we really wanna find a way to be... to support as many people in doing this as we can. I think we're still, like, learning. Like, a lot of the industry, like, has evolved. We've seen, you know, a lot of different pieces, um, get spun up v- and spun down, and I think the, the operative part for, for Katelyn and I has been in the category of, like, making sure, at least at the base layer, that we provide as many primitives across the board as possible. So, you know, this kind of like, yeah, like, knowledge, execution, coordination layer, we wanna give all of that out to everyone so that people can start to compose and create on top of that. Um, and that's just from a, I think, pure builder kind of point of view. Then there's a point of view around, like, how do you kind of like plug in with us, right? Like, we're also building first-party products of our own. We've also created some ways to embed natively with us, like for example, connectors, which are built on top of the MCP spec. Um, and we try to be more open about those types of things, and we're starting to figure out, like, what are the right bits and pieces. But, you know, what we're really trying to do is get to a place where, you know, in a, a, a company is able to get created and built on, uh... they can build whatever products that they want, they can build agents if they need to, and then those agents and those products could be things that could plug into other agents. Some of those agents could be Claude agents, some of those agents could be other people's agents. Um, but we wanna be able to enable that kind of like transactability across the board. And then I think in order for all of that to kind of ultimately be true, there is a bit around, like, standard setting, and I think there's the traditional standard setting, which is around, you know, how do systems interoperate. Um, and that's, uh, you know, things that you've kind of seen us do with like Skills and MCP. But they're at, again, like the builder layer. I think at a higher order layer, there's also a bit around interoperability and standard setting around how do we all kind of like treat safety together. And, you know, we've talked to a lot of these companies, and this is less from, you know, uh, uh, philosophies aside, just more like no one really wants to have technology that's like, for example, like, doing negative things on, um, on their service, right? Uh, so cyber, I think, is a great example of this. Uh, you want to protect your own systems from, like, negative actors or, or bad actors. And so, like, these kinds of, like, standard settings of, like, how can we find ways to partner with more and more people to be like, "Yeah, we all kind of want to make sure our critical infrastructure is good. We all want to prevent, like, fraud or any of those things from happening, and how can we work better with each of these members?" I think on the last layer, we're still kind of like... we're still evolving, and I think we're still very much, like, trying to find ways that we can be better and work with the rest of the industry to bring people along and, and work with them. Um, but those are kind of like, you know, the higher order primitives or pieces that we wish to kind of like be in place, um, so they can work with folks to, to ultimately solve this. I think if I were to like take a step back at the end of the day on, on all of these things, um, you know, like, this technology is so transformative. Uh, and if it's a little bit like electricity in the sense, like before electricity, there was just like, you know, you had to like have a candle, and it was like you could only do so many things. Um, but with electricity, the reason why it's such a transformative technology for all of us and so greatly of a utility is because you can actually, like, wire it into everything. Everyone is able to actually access it. We also have, like, standards and ways to plug in and do all the pieces that we need, and that's not something that anybody can do by themselves. They always have to work with the ecosystem and work with partners, um, to figure out a path forward.

Sonya Huang 15:39 How do you think about the philosophy of building an open ecosystem, uh, versus a walled garden? And, you know, how do you think about what products are really important for you to own first party versus where you're perfectly happy to plug into other components of the ecosystem?

Katelyn Lesse 15:54 Yeah. There's... So maybe in using Angela's kind of layered cake that we talked about a little bit earlier, you'll see that on some pieces of this, like execution, for example, um, what we've done within something like Claude Managed Agents, and I think over time you'll see us try to make this a little bit more modular. We actually aren't precious about, "You should run these things on our infrastructure." Like, it should be sandboxes that we control, or it should be a storage layer that we control. Um, well, we actually, like for example, we launched self-hosted sandboxes, and we partnered with Modal and Vercel and Cloudflare and a bunch of other folks, um, even like Amazon's new microVMs, um, to have a first-class offering where you can go plug any of those things in. Um, we launched MCP Tunnels so that you can call out to your MCP servers that are behind your firewall, right, and, um, be able to punch through there. And so for some of these things, we, you know, the whether, whether it runs on our infrastructure versus somebody else's infrastructure is actually not important to us, 'cause the thing that's important to us is more that the architecture of how you put together these agents in a way that will be powerful, in a way that will be reliable and scalable, um, we have strong opinions on that, and you can kind of just conform to the interfaces that we put out there and plug those things in. Um, and we think that that generally is a thing that works

Katelyn Lesse 17:12 really well.

Angela Jiang 17:13 Yeah. I think on the, the kind of like verticals where we might build products, um, you know, I think we, we kind of have, like, two frames here. The first one is we are always trying to figure out a form factor, like an evolving form factor. We, by the way, don't think form factors are, like, static. It's like a dynamic thing, so what might be awesome for one year's worth of AI development will probably not be awesome for the next year's worth. And we just kind of try to have that mentality. We tell the team, uh, just overall, like around Anthropic, everyone's always trying to be like, "Is this AGI pilled enough?" Um, and then w- we always have this mentality of, like, you know, we build something, it works, it was cool for a year, and maybe it's not the right next thing, and so throw it away, try again. Um, and we, we tell, like, platform users the same thing. Um, I just think that's probably just like, you know, attached to the technology. But, so yeah, one, one principle is, like, trying to always constantly find this new form factor. So sometimes we'll, like, launch products in certain areas to try to showcase a new type of form factor. Um, it's not necessarily 'cause we think it's, like, the biggest TAM or the most important thing to go after, but sometimes you're like, "Okay, this has, like, always been a really difficult thing, and people have always communicated this way or tried some things this way, and can we show that maybe there's a slightly different way? Um, and because the model capabilities are, are so advanced now, can we try to express it a bit differently?" Um-

Sonya Huang 18:24 What's an example of that?

Angela Jiang 18:25 Yeah. You know, like, uh- Claude Design is a little bit of, of that way. I think depending on how you squint, you might see it as, like, a way that we kind of are going into design as, as, uh, as like, you know, one of the verticals. But more often than not, it's like if you take a look at what we're trying to do with that product, there's a couple of, like, decisions that were made in there. The first one is that, like, you can actually try to offload more and more and more to Claude. Um, and so it tries to be kind of opinionated on like, you know, just, just, like, talk to it, and, like, let it really try to figure it out. And yes, you can still edit it and then do these kinds of things, but kinda like discourage a little of that, and more just like let... Just talk to Claude to go figure it out. Um, the second thing was it was really trying to express that actually, like, code is a, is a, a way to solve for things that you wouldn't normally think would be the way. So a lot of people who have built kinda generative, um, you know, like slide decks or designs or whatever, um, will pick, uh, the way of, like, they have, like, some kind of design system. You integrate against the design system. It's almost in the traditional, like classic WYSIWYG style of designing something. And with, like, Claude Design, it was like, okay, can we try to just, like, use code purely? Like, have Claude generate that code, and would it, like, do a good job? And we found through some experiments early on, it's like, actually, it looks like it can kinda do that, and how can we kinda showcase that, uh, to the world? So that's, like, an example. We have a lot of other internal projects, and this kind of falls in the category of, like, expressing form factor. We'll all try it out internally. It'll be super cool for like two weeks, and then we move on to the next thing. Um, we never even ship the thing, frankly. But yeah, we actually do a lot of product experimentation in that area, and that's, like, our labs team. And then there's, like, the second category, which is that we actually do look at TAM. Like, we're a business. We do look at TAM. We do look at areas that we think, uh, you know, there'd be reasonable agentic, like, operations that would happen. In those areas, uh, we do tend to have an orientation towards things that are more token heavy. And by token heavy or token hungry maybe is the way I would say that, is, like, what we mean is, like, you know, you... For spending, once you spend a, like, call it, like, one turn, you look at the end of that turn and you say, like, "Am I done, or am I actually so glad that I did that thing, I want to do more of that thing?" We like industries where it's like the answer to that question, you say, "I wanna do more of that thing." So coding is obviously the one that we all know. And the great thing about coding is that what it's actually doing is that, like, once you've finished a turn, you look at that, and you're like, "That was incredible. I'm, like, unlocked. I'm going to do, like, more. I'm going to build more. I can do more." And there's other services where it's like, actually, when you finish that turn, you completed the job, and you just move on. You know what I mean? Um, and so we tend to, like, go into the ones that are a bit more like there's this kind of, like, iterative flow. You're going to build more, generate more together. Um, and then the last angle that we kinda take a look at is just sort of like, you know, there's gonna be certain business functions that we're like, they are the buyer that we like to go to. We want to help them optimize their workflows, help them create better products there. And I think we've been pretty transparent with some of the verticalization, like we've done, like, finance, we've done, like, legal. Um, and we've tried to kind of, like, narrow on into specific areas where we feel like by having the right context and the right tools and putting it together in a good form factor is probably useful, um, for us to, to be able to do.

Katelyn Lesse 21:25 And in each of those areas, we do... We're trying to do a bit of, like, showing the art of the possible across all the different ways that you would accomplish those outcomes. And so for, you know, like finance, for example, is a good one. Um, you know, we-- You could be a company that solves problems in finance, and you could build directly on the messages API, and you can just get some tokens, and you can build everything else on top. Or you could be someone who builds on Claude managed agents, you can get a lot more out of the box. Or you could say, "I'm gonna build a plugin that..." Or, like, a connector, right? That's gonna sit within one of our products and within those form factors. And what we did recently, we launched, like, Claude for financial services, is like, okay, cool, we've got packages of skills and things like this that you could choose to use within our product, within other people's products. We even launched, like, cookbooks on here's how you would use Claude managed agents to go and do these things. And so I think for us, it's all kind of an experimentation around, like, you know, we provide people all these different pieces and see kinda where they run with it.

Katelyn Lesse 22:26 And then sometimes we put together products that are just packaging of all of these things. Like Claude Tag, I think, is a really good example. Like, we had been seeing people in the industry go and say, like Shopify did this with River. Um, Square, Block recently did this with Builderbot. Um, there's, like, a few of these examples where people said, "I'm gonna pr- I'm gonna build, like, an agentic platform internal to my company, and I'm gonna try to give it all the right context, and I'm gonna make it accessible from Slack or from various other, um, you know, platforms that you'd want it to be accessible at." And I think Claude Tag was very much a packaging of all those same things that anybody could choose to build something similar, but this is how we're kind of like, "Well, this is how we're doing it internally, and if you would like to just kind of plug in and go, here's what that looks like."

Angela Jiang 23:11 What do you think people have misunderstood about Claude Tag? 'Cause there was all this, like, ruckus about-

Katelyn Lesse 23:15 Yeah

Angela Jiang 23:15 ... "Oh my gosh, it's just a Slack bot?" Like, tell, tell us what, what the magic of Tag is.

Katelyn Lesse 23:19 [laughs]

Angela Jiang 23:19 No, I think it's a great question. Um, and I, I do think it actually showcases a little bit of where maybe the future could be going. Um, yeah, I think, like, the... I think if you look at products in the past, people are like, "Oh, you really attach to, like, the form f- or the, the UI almost, right?" Like, it looks like this, so it's, like, super cool. Um, and I think when you look at s- like Tag, uh, it, like, yeah, like the way you interact with it is that you, like, literally tag it in Slack. Uh, and so yeah, that is, like, the interface. But that's not really the important part. The important part, um, is all the kind of, like, context engineering and, like, architecture that we put underneath the hood so that Tag just works. It really should just, like, just feel like a coworker. Like a co- you know, if you go to a company, and you onboard, the, the coworker comes into your channel, and then you can chat with it. It's proactive. It figured out, like, what's, like, useful. You c- and, um, it just gets stuff, like, done for you. And so if you think about, you know, especially, like, non-technical audiences, this is like, it's a huge unlock. You just, you literally create a channel, and then you @Claude, or sometimes you don't even @Claude, and you're like, "Hey, I wanna be able to do this and do that, and I can't figure out this, and how do I actually, like, submit an expense report again?" And traditionally, if you think about how to solve that workflow, you are going all over the place, and you're talking to your manager, and you're talking to your spinner buddy, and it's, like, really, really complicated And, uh, today now you just, like, go talk to Claude Tag. And we do a lot of the hard work on doing the context engineering, the proactivity, a lot of the harness pieces. I think Andre Capartay said it really well. He's like, "It's like an org level harness." There's a lot of, like, complexity baked into that. Like Katelyn mentioned, like you can use our APIs to go and construct that. You have to do a lot of the experimentation yourself obviously. But this is like an opinionated take from Anthropic on, like, how you can have this really awesome always on, uh, kind of agent for your entire, entire company. And the bit that's, like, futuristic, I guess, is, like, a lot of that complexity is actually like... It's like an iceberg. It's like all the stuff underneath it, that's actually becoming the harder and harder and, like, useful part that we're trying to, like, push through. And I think we'll see more and more, like, that kind of, like, tip bit that's, like, outside in the water. It's just, like, the interface can actually constantly swap. Like today, right, like Slack is a place where a lot of people collaborate, a lot of business collaborate, but also a lot of people collaborate in teams, and some people collaborate by a WhatsApp group, um, or they text each other, or they... Some people still email each other. And, like, those could be the form factors that actually completely you can imagine agents just going there and being, and they're almost taking up the same form factors as humans have taken up. It was almost like a very, almost, like, boring take, but it's actually, like, I feel like the most, like, forward one because you want the agent and you want AI to basically be like another person, and it's helping you. But it's like, you know, very intelligent, can figure out all the context, and you can always have it to be a really helpful assistant.

Sonya Huang 26:03 Totally.

Sonya Huang 26:04 You talked about context and then harnesses quite a bit.

Angela Jiang 26:07 Mm-hmm.

Sonya Huang 26:07 And so, uh, your team is just, you know, has such an opinionated point of view on, like, what it takes to build an exceptional agent. I imagine a lot of that comes down to the context engineering and the harnesses.

Angela Jiang 26:16 Totally.

Sonya Huang 26:16 Maybe, like, what best practices of, or advice would you, would you share with people about what you need to get right on the harness and what you need to get right on the context?

Katelyn Lesse 26:24 Yeah, I think... So it's interesting because we've kind of talked about, you know, we launched Claude Managed Agents as this, like, very generic but high performing harness because we've done all the nitty-gritty work that's actually, like, really boring and not super interesting around how do you deal with prompt caching, how do you deal with context management. You, like, clear old stuff out of the window. Sometimes you, like, call tools programmatically, so you don't pull everything into the context window, and you can keep it clean. There's a lot of those sort of details on the lower level harness layer. Um, and I think honestly, like, best practices are just stuff like prompt caching, do it. You're gonna save a lot of money and, and token costs. Um, obviously, like, try to keep your context window clear, and then putting those things together in, uh, a harness that will be performant is, is, you know, sometimes specific to the task that you're trying to accomplish, right? And then, of course, evals. Um, I'm surprised we got this far into this thing before one of us said the word evals, but, like, you need evals, um, to make sure that what you're trying to accomplish is performant. Um, but I think where we're starting to go, and Angela mentioned this a little bit earlier, is more of a concept of strategies or meta harnesses, because I do think that, yes, you can, again, make this lower level harness that's gonna be performant, and maybe that's interesting for you to do yourself, or maybe not, and you offload it to us. But this concept that you can take any given token and spend that token on just executing, or you could take that same token and choose to actually reflect on your past agentic sessions and write learnings to memory so that the next agent does a good job. Or you could take that token and advise with a bigger model so that a smaller model can execute and do a better job. Um, or you can say, "Execute, execute," and then, like, a grader comes in and is like, "Did you do a good job? No, you didn't. Try again." Right? And so I think the, the, like, interesting innovation is gonna come more at that higher level on, like, the meta level, right? And I think optimizing within those strategies is something that our team is really excited about, and we're starting to do a lot of work there. Um, and I think a lot of other people are starting to feel really excited about this concept of strategies and, like, the jobs you give to tokens, because again, like, yes, there's best practices on stuff like your prompt caching and exactly how you clear stuff out of your context window and how you write your evals and, like, a lot of things like this. But I, I don't know that there's necessarily so much juice to squeeze in a lot of cases out of that layer as compared to a layer higher than that.

Angela Jiang 28:53 Yeah. And one of the reasons for that, I think, is it has to do with the generations of the, the models. I- if you look, like, two years ago, a lot of the harness was, like, a scaffold to kind of, like, tell the model to go from point A to point B. And you had to, like, you really had to, like, build in a lot. You'd practically build one wall here and one wall here so that, like, the thing would go in a straight line. And now the models are actually very, very steerable. Um, and so a lot of that steering, you can just put in the prompt, right? Like, "Go do... Go from point A to point B," and the model, like, will go from point A to point B. So a lot of... If you have harnesses, um, that are, like, designed to kind of do that kind of, like, steering, you can delete that part. Like, that part we actually frequently encourage where you could delete part of those harnesses. I think various people have said things along those lines. And that's, I think, what people oftentimes mean when they're like, you know, the model will kind of consume some of the scaffolding. And, like, in that sense, like, for sure, if your scaffolding is telling it to go in a direction, um, that it can just intelligently figure out, like, that I think will increasingly continue to, to be so. But as a result of, of this, what the harness needs to start doing is more allow it to run longer. And so that's where, like, that execution bit tends to be, I think, like, it sounds like a maybe somewhat silly point, but I do think it results in a lot of differences because, because it, you can go in the direction that you tell it to go, you obviously don't want it to stop at B. You're gonna be like, "Okay, now go from B to C, and then go to F, and then go to Z, and then come back to me on A." You know, something funky like that. And in order to be able to do a lot of those things, the kinds of harnesses that you do are less the steering harness, and it's more like these kind of strategy harnesses that Katelyn's mentioning, which allows you to operate at a slightly higher level of thinking, which matches, I think, a lot of the intelligence gains which we're starting to see with the model.

Sonya Huang 30:28 Do you think task specific harnesses make sense, or, uh, vertical specific or task specific harnesses?

Angela Jiang 30:34 I think people have different opinions on this. Like, our opinion is yes. I don't think there's, like, a general harness. I think there are some capabilities that are obviously very general, and they tend to, like ... be very useful. Uh, like coding is a capability that, like, is very useful because you use it across so many things, and software as, uh, you know, just, like, eaten so much of, of what is capable. So our ability to, like, write software is therefore useful. I think when you think about, like, very, very specific types of domains, they're going to require, like, a couple of pieces of the harness to be sort of, like, customized. One of that, uh, I do think, is how you choose to kind of, like, handle sort of, like, errors, uh, between when you do something and you hand something off to the model. Um, so in, like, domains where you require, like, an extreme level of verification, that logic of how you handle that ver- like, it, again, I think it sounds small, but, like, I totally understand why some people feel like they really want to own the harness, because tweaking that last bit will give you a ton of juice, and especially domains, like, like legal and finance, where there's a lot of consequences, um, to you not getting it perfectly correct. Like, it's really gonna matter, and that's gonna be the difference between your product and someone else's product being the thing that the user ultimately uses. Um, and then there are other domains for which, like, I would say, uh, it's not going to matter as much because you're able to compress it into, like, a general model capability. So the tweaks that I guess, like, you know, where, where we feel like the domain specificity is really gonna matter is the specific, like, verification logic between the model and your execution, and then, um, I think it's gonna be about, like, some of these kind of, like, higher order strategies on how well, um, you're able to actually, like, allocate your token budget. Um, I think the context bit is actually a little, like, overdone. Like, yes, you're gonna, like, throw in context, and, like, that's... Uh, but y- any harness can actually handle a lot of context, and so that's just more, like, you have the data, and if you have the data, then obviously you're, you're uniquely qualified to do something useful.

Katelyn Lesse 32:24 Yeah, and I think when people say harnesses, they often mean a lot of different things, and I think this is why, in part, there's so many-

Angela Jiang 32:30 Yeah

Katelyn Lesse 32:30 ... different opinions on this. Like, you can think of a harness as literally just, like, a loop, um, that's like, okay cool, like user, model, user, model, tool, you know, like that sort of thing. Um, then you could think of the harness as also all of the tools that are packaged up with the harness, right? And, and there's just, like, a lot of different definitions of these things, and I think the stuff that can be pretty generic and, like, less interesting to own and, and deal with, is what I was kind of saying earlier, is like getting your prompt caching right, right? Like, maybe that is not the world's most interesting thing. Choosing to clear out old tool calls from the context window and, and things like that, right, are, like, maybe a little bit less interesting. And you, like, go a layer higher into some of the stuff Angela's talking about, and then you get into, like, okay, yeah, these are things that I might want to own and control. And so it's interesting with Claude Manage Agents, like the thing that we built today, we call it higher order, but it's not really, like, that high order in the sense that you can choose to define all of the tools that you want to bring in as custom tools with the harness, right? And, like, we give you a lot of knobs to control. You can define skills, you can do your system prompts, you can do a whole bunch of different things, MCP servers and things like this. And I think where, you know, we want to get to is a point where you can literally just tell an agent, "Here's the outcome I want, and here's the budget that I want to spend," like ready, set, go. And you maybe, like, don't think about any of those things underneath. And so I think there's just a few different layers of this, right? That for certain things, like, you might want to sit at a different layer of what you actually go and control. Um, and you can probably get better outcomes within some of those layers by doing a little bit more optimization work.

Lauren Reeder 34:07 Very cool. One of the things I'm curious about, and one that I love about infrastructure and platform teams, is that you get to see what the most advanced users in the world are using and learn from them. I'm curious, what are some things that you're seeing and learning from, from the people building on your platform?

Angela Jiang 34:21 There's some people that have been doing some really funky ways of, like, handling context. Um, we ourselves explore this a lot. That's actually, like, one of the reasons why Tag is, like, uh, such a great product, is, like, there's a lot of really awesome, like, context kind of engineering that, that's happening. Um, we've seen some teams be really clever about, like, how they do that, and they are able to kind of think through, like, "Okay, if I have all these contexts in a bunch of different places, how can I proactively go reach out to them? How can I try to generate enough, like, um, permissions across each of them s- and then feed that all into, like, an agent?" And it's interesting that, like, um, I guess, like, this is kind of the level of innovation that, like, we're actually, like, very excited by. It doesn't express itself as, like, a completely different product form factor, um, but what it actually does express itself as is, like, maximally useful to users. And we've been seeing this more and more with, like, inter- actually, like, internal use cases l- more, instead of, like, external ones. So, like, companies who are becoming more AI native, basically, they're the ones we're seeing increasingly more and more innovation out of. And so, you know, we've had, like, customers try to do this for their... Like, they've built their own, like, custom SDLC kind of setup in very, very innovative ways. We've had c- uh, ones who do that for, like, their entire back office. And just, like, the kind of nuances of how they, like, stream in context, I think has been, like, actually really interesting in terms of, like, how they've been putting together the pieces. So that's been, like, one category that's been, like, really, really, like, fascinating. Uh, another category that's been, like, really interesting has actually been with companies that are dealing with, like, really old school software. And so there's a lot of, like, healthcare companies, um, that we kind of engage with, and, you know, like, they're like, "The, the systems I'm working with, they don't even have APIs. Like, that's, that's a, a dream." Um, and so, you know, how can they use computer use, uh, and things like this to be able to start to kind of automate and create more connectivity with our systems? Um, and that area of innovation, I think, has been really exciting. It's been really interesting to see people try all sorts of crazy stuff, from, like, taking a laptop and trying to, like, run a bunch of things on it to auto-generate a bunch of things that then their agents can go and use. Um, and this has actually been probably, like, an area of, um, I think a lot of innovation coming from a lot of our customers that we want to find ways to, like, support better and see, like, okay, maybe there are... Like, how can we make this easier for you? How can we help you with some standardization? How can we get it so that, you know, like, you can just have a spec, and then Claude can then respect it, and so it's much easier for you to organically connect a lot of these things? But yeah, maybe the, the general theme I would just give you is, like, interestingly, a lot of the innovation that's most exciting out there right now has been, uh, this kind of, like, context and connectivity layer, which has been really fascinating.

Katelyn Lesse 36:49 Yeah, like, a good one in that, um, we were working with a customer who they built some agents on Claude Managed Agents. They also have some agents that they built on other models and other platforms, and they've kind of optimized each of these agents to be good at the things that they want. They want these agents to all be able to work well together. Um, and they kind of were like, "Wow, galaxy brain, like, what if I expose an MCP server on top of this agent so that it can then go and, like, have this other agent call a tool on that agent, right? And, and have these things just be more modular and be able to work together." And we were like, "Yeah, totally." And we sat down with them and worked through it, and, and it worked perfectly, and it was pretty cool. And so we're seeing a lot of, again, that connectivity layer that I think is one of the cooler areas where people are innovating. But- Outside of that, one thing that has been cool is just seeing the shift in, I guess, like, industry trends of where we're seeing a lot of our usage come from. Like, we talked a lot about coding, like coding as a category, like of course absolutely exploding. There's so much going on there, and we're starting to see some of these emerging trends. Like more recently, um, we're starting to see manufacturing really pick up as just a category where people are building with AI, and like one of our PMs, like getting on a flight to Detroit to go [chuckles] like figure out what these customers, like what they need and what's going on. And so I think we're gonna start to see a lot more just kind of like outside of the box of what people think about today sort of use cases, which we're really excited about.

Sonya Huang 38:12 Hmm.

Sonya Huang 38:13 It seems like there's now... There was a... We went through a token maxing moment of history, and now there's like the token rationalization moment of history.

Katelyn Lesse 38:21 [laughs]

Sonya Huang 38:21 What are your thoughts on that, and like what, what should companies be doing, and then how, how does the platform team think about, uh, enabling that?

Angela Jiang 38:28 Yeah, I mean, it, it makes sense. Uh.

Sonya Huang 38:30 [laughs]

Angela Jiang 38:30 It, it makes sense, uh, from the high, you, you start to rationalize. I, I really like that framing. And I think there's like a couple things that, that are like top of mind for us on this front. I think like, again, it makes sense, and as these models get more and more capable, you're gonna hit like levels of intelligence max- maxing that are like there, that then you wanna do the next kind of dimension. And the next dimension after intelligence will either be cost or it will be speed. Um, and you just kind of, you know, go through that across all possible task complexities in the distribution. Um, and as we kind of see that like happen, you know, something that's like really top of mind for us that we kind of try to spend some time with users on is like, what you don't wanna do is like stop AI usage, right? Like, that's kind of the wrong move. And we do actually see some of our, our customers do that. So oftentimes, the way that AI spend has erupted inside their company has been through some kind of like, uh, shadow IT. You know, like, their employees just like wanna use it, they find a way, they end up procuring it themselves, and before you know it, like half your org has like found some way to install Claude code. And in that world, it is kind of hard to, to manage, 'cause these things are, again, like they're very token hungry ultimately. And so what we try to kind of encourage our customers is like, you don't wanna like stop the innovation. Like, if you are getting returns on top of this, you are shipping faster than ever before, you can like run more operationally, like, uh, efficient, then those are gains. And so the area that we actually try to encourage people is like if there is a way for you to kind of construct, again, like a strategy that allows you to design an architecture that says like, given a task, assess its level of complexity. I mean, I'm effectively def- d- describing a router, but like there are ways to do this that are like, I think, a bit better now. And so like this task comes in, has a certain level of complexity. For that level of complexity, like you can define some rules, but for the most part, right, if it's like a hard task, you should probably route that to like a big super smart model. And if it's not a hard task, you can route that to like cheaper models. Um, designing that, I think, has a little bit of like... There's a lot of technical complexity in that, but it's like very, very doable. And we actually like encourage people to try those kinds of things. I think ultimately-

Sonya Huang 40:19 Do you think you'll offer a router?

Angela Jiang 40:21 I, I think within the Claude space, it will like make sense. It's actually one of the strategies we imagine like designing, 'cause the way that we're kind of thinking a lot of these things is like, it almost feels like every month there is a new era of something. Um, and if we just take a step back, like okay, and it, this seems to be like really fast. And so what are the different ways that are recomposable so we can redesign very quickly for any new whatever the cool thing is that month kind of like bit. Um, and so this is like in that category of things, where we feel like we can actually just like recompose a lot of our primitives and then design it. I think the bit that we do feel really strongly about on the model routing front is like we are designing our platform for Claude, and we wanna make sure that Claude is great at like solving all these things. So we'll like restrict to that space, um, rather than... You know, I don't think we're that interested in saying like, "Okay, and then, you know, you should route to a different model," or whatever.

Sonya Huang 41:11 Makes sense.

Katelyn Lesse 41:11 Yeah, and, and, well, some of that too is just like, I think we have a strong belief that harnesses and, and just like the agentic layer should be tuned to the model family that you use it with. And so I think there was a period where people were kind of like, "Yeah, cool, I can like build a harness and build an agent, and then just like plug in a different model underneath," and they were excited about routers from that perspective. And I think we've started to see, um, like Vercel just did this with Harness Agent, for example. Like, some of these players in the space like come up a layer of abstraction and say, actually, like plug in the whole harness and the whole agent that's tied to a model family, which makes a lot of sense. And so what we could provide is a little bit better, smarter, like how do you mix and match the right models within the model family underneath that thing, if that makes sense. But yeah, on the general question of token maxing costs and these sorts of things, I think we're just kind of going through what feels like a normal, natural cycle for companies in figuring out how to make the best use of this technology and run their businesses really well and really effectively. And, um, it's interesting, like before working at Anthropic, I was at Stripe, and we were kind of in the very reasonable era of like we paid a lot of attention to our AWS bill. [chuckles] And so, you know, if someone were to have built some background job and they like didn't quite configure it correctly, and this thing's like burning through like CPU or whatever it is, right, like at any given moment and causing, you know, a big increase in spend that's not actually worth it, right? Like we have put in place the guardrails to find that and then go ask that engineer very nicely to please turn off their background job [chuckles] that's not like within the bounds of, of what they should be spending for the thing they're trying to accomplish. I think those are the things with AI that people are gonna start to go and figure out. And I think to Angela's point The thing that gets dangerous is when you're kind of just like, "Here's a cap," and you're stuck within your cap, like ready, set, go. But I do think that encouraging innovation, encouraging people to, you know, create really excellent outcomes with this stuff, and then coming in from the side and looking and saying like, "Okay, well, there are a few different ways that we probably could have accomplished that outcome," right? And one is, like, you take Opus, and you run it all night, and you do something crazy, and another is maybe to get a little bit sh- smarter with the strategies that you put together in order to create that same outcome within a lower cost. And I think that's the, like, next layer of thinking that everyone's gonna start to do.

Sonya Huang 43:31 Very cool. Is there anything that you guys are excited about building over the next few months that you can share a hint at what might come next?

Angela Jiang 43:37 Uh, yeah, I mean, I know we said this word, like, 20 million times, so I apologize, but, like, we really are trying to build ways for you to compose strategies. Um, and so, uh, that is an area that, that we're, like, trying to move into that kind of like, yeah, uh, coordination layer of the abstraction. Um, and we want to start at, at this front because the types of problems that we see people building, they are at a layer where it's like, in order to get the most return on this, you have to be a little clever about, like, what is the nature of the problem that you're solving. So to give you something, like, concrete, like, and when you try to solve for, like, let's say you want to build an agent that's, like, trying to, um, do bug hunting, and you could just send one off to go and do that, and it's gonna give you a certain type of return, a level of return, uh, possibility. Um, and then people kind of get stuck at that, and they're like, "Okay, my next options are I can, like, make a bigger... I can just, like, swap the model for a different, uh, probably bigger model, um, or I could, like, let it run, like, longer." And that's, like, pretty much, like, the only two, like, levers that you have to, like, try to make this, like, bug hunting agent. From a lot of experimentation, when we do these kinds of things, there's, like, actually, the thing, like, those two, those two things are still true, but you actually have, like, a third lever, and it tends to actually do a lot more than you think it does, which is that actually, if you were to, like, best of end the thing, it would, like, give you a lot more returns. But, like, just to be... Just saying those words are fine, and there's plenty of papers, and people have published it. To actually build that thing and put it into production, so you can actually test it on users, uh, and see the results for yourself, that's, like, really, really freaking hard, and you end up building all these, like, custom harnesses, so on and so forth, or like, you know, all that stuff. Um, but we're seeing, like, this is where the alpha is, and it's hard. And so, like, in the same very simple philosophy that we talked about at the beginning, like, if it's, like, gives you the return that you want and it's hard, we're gonna go try to just make it easy for you, so then you can use it to then run the experiments you actually need to run.

Sonya Huang 45:20 It reminds me of when people were talking about agent swarms a year ago. It's some version of that.

Angela Jiang 45:25 Yeah.

Katelyn Lesse 45:25 Has it been a whole year?

Sonya Huang 45:26 Yeah. [laughs]

Angela Jiang 45:27 Yeah. Oh my God.

Sonya Huang 45:27 I know. We're finally there.

Angela Jiang 45:28 Yes.

Sonya Huang 45:28 [laughs]

Angela Jiang 45:29 Um, yeah. No, I think that that's, like, that's a type of strategy.

Sonya Huang 45:32 Yeah.

Angela Jiang 45:32 Exactly.

Sonya Huang 45:33 Mm-hmm.

Angela Jiang 45:33 In the same way that you have, like, you know, one big one that separates a bunch, that's another type of strategy. And I think people have thought about this maybe the q- in the way of, like, human organization. I guess m- it could be similar, but if you take it to kind of its end state, it's actually more just like the token has a job, and I think it's this job piece that we're, we're really indexed on. And, um, we see a lot of returns too, and that's the thing that we want to spend time with users and the rest of the ecosystem on, on, like, how can we just make that easier for folks to then experiment? Like, we can give you, like, five jobs off the top of our head, and we'll probably, like... That's what we have internally. Um, and if we give this out to the rest of the ecosystem, it's probably gonna be, like, 100,000, 200,000. Who knows what other combinations that people could put together.

Katelyn Lesse 46:10 Yeah. We want to be able to keep doing this hill climbing on, like, how do you get the most value, the most intelligence per dollar, and just put that power in people's hands. But around the edges of that, we have these personas that have kind of just, like, things they have to work through in order to be able to, like, really deploy AI either within their companies or within their products. And, um, that's, like, the sort of enterprise-ready security and compliance controls and things like this. But really, even just, like, making the platform more modular in the right ways, like being able to plug in different pieces of the solutions that we're building. Like, I want to use memory for this thing over here, right? Or whatever else it is, and having a truly excellent developer experience around that because we spend a lot of time with enterprises who are like, "Okay, I have this, like, walled garden. I need to figure out exactly how I can plug these solutions in." And so we're, we've got a part of our team that's innovating on things like strategies and jobs and trying to help you maximize intelligence, and they're like, "That's really cool, but I can't actually use any of that for X, Y, Z reasons." So I think solving those problems is really, really important to us. But then the other persona is, you know, the, like, weekend developer who's like, "I want to go and build something useful for myself," right? And they're often doing that on top of our platform and on top of many other just pieces of developer platforms in the community. And I think for some of those folks, there's more that we can do to be... provide solutions that are maybe more open or more hackable or whatever it might be for those folks to kind of just, like, go wild with what we can offer them and have this really excellent developer experience. And so I think there's a lot of stuff that maybe I would put in the, the category of table stakes that I'm really excited about because I think those are the things that then unlock getting people to say, "Okay, yes, this thing works for me, and now I can plug in on some of the stuff that you guys are doing that's really innovative and hill climby to get more intelligence and save costs and things like that."

Sonya Huang 48:01 Wonderful.

Angela Jiang 48:02 Awesome.

Sonya Huang 48:02 Katelyn, Angela, I feel, I mean, you are building one of the most important developer platforms in the world, and talking to the two of you over time, I just feel really optimistic that that platform is in very thoughtful, uh, hands that, that care about the ecosystem. So thank you for taking the time today to share what you're up to, and, um, we look forward to what's ahead.

Katelyn Lesse 48:21 Thanks for having us.

Angela Jiang 48:22 Thank you.

Sonya Huang 48:22 Thank you, guys. [outro music]