# How Anthropic ships Claude with Claude: Separating the Brain from the Hands

> Source: <https://latent.space/p/opus55/>
> Published: 2026-10-27 18:53:10+00:00

As we learned from [the Anthropic S-1 filings yesterday](https://latent.space/p/ainews-anthropic-s1/?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv), the lab has some of the highest revenue and research efficiency in human history, particularly with -126% loss to 19% GAAP profit turnaround in the last year. The company credits this to their two core loops: Improvement and Investment: 

**Today we go inside how Anthropic works.** Now the dust has settled on [Opus 5.5 and it has lasted a month](https://latent.space/p/ainews-claude-opus-55-the-new-default/?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) as the generally accepted #1 LLM in the world, we dive into its launch, and how Anthropic ships Claude with Claude with **Katelyn Lesse and Angela Jiang**, heads of Engineering and Product respectively. 

The team is working under two kinds of pressure. The models are getting more capable, which makes safe deployment and access decisions harder. At the same time, usage is growing fast enough that ordinary infrastructure choices—like how much CPU an API consumes—become strategic. Those problems meet in the product choices that reach developers.

## A model launch is also an API launch

 Anthropic's [Opus 5.5 launch](https://www.anthropic.com/claude-opus-5-5?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) emphasizes both model quality and cost. We discuss feedback that the previous Opus generations were be too verbose, alongside the need for stronger intelligence per dollar. Anthropic says the new model costs less to run than its predecessor; the exact saving depends on the workload and its use of cached context. 

The less visible launch work involves changes to how people integrate the API. One example we discuss is protecting model reasoning against extraction used for distillation. Preserving thinking blocks across turns helps address this, but a new requirement can also break a customer's existing harness.

 Katelyn also describes what being part of **early access programs** actually looks like in practice: Customers run evaluations in their own harnesses, report surprising behavior, and sometimes pass along less formal impressions. The useful part is not just a benchmark score: it is seeing the model in environments and tasks Anthropic's own products do not cover. As we have said [for 4 straight years now on the podcast](https://latent.space/p/braintrust/?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv), **having good evals is golden.**

## The brain and the hands

 Our favorite mental model in the episode is the distinction between an agent's brain and its hands. In [Managed Agents](https://www.anthropic.com/research/managed-agents?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv), Anthropic separates the session log and harness from the sandbox that executes code. The harness can persist while an execution environment is created when needed; a failed sandbox does not have to take the whole agent session with it. 

 We covered this in [the Thariq pod](https://latent.space/p/thariq/?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv): 

 The same separation gives security a useful architectural property. If generated code runs in a sandbox that never receives a service credential, there is no credential in that sandbox for the code to read. The episode describes credentials held separately and attached through a proxy, alongside controls on where sandbox traffic can go. Anthropic's [containment write-up](https://www.anthropic.com/research/how-we-contain-claude?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) describes the wider set of isolation and network controls used across its products. 

The distinction matters for performance too. The guests say an earlier approach put the whole coding agent in a sandbox behind an API. Starting the environment and agent added work before the first response; if the sandbox disappeared, so did the running agent. Separating the pieces allowed the platform to pursue lower latency and better reliability while keeping execution isolated.

It is a helpful lens for anyone designing an agent runtime. Ask which parts need to survive, which parts execute untrusted work, and which secrets actually need to be present where code runs. The implementation will differ between products, but those questions remain useful.

## An agent that helps another agent remember

 Managed Agents also provides a base for experiments in [memory](https://platform.claude.com/docs/en/managed-agents/memory?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) and multi-agent workflows. [Dreaming](https://platform.claude.com/docs/en/managed-agents/dreams?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) is simply implemented as one agent inspecting another agent's prior work and writing memories back. That makes the feature an example of building a higher-level behavior out of the same underlying managed-agent primitives. 

Their account of the roadmap is more nuanced than a list of features. Memory and learning remain important, but the team says more of its immediate attention has moved to safety as model launches have become harder. They also discuss keeping the base harness relatively simple, while letting developers experiment with verifiers, parallel agents and other strategies on top. Ideas mentioned for future platform capabilities should be treated as plans, not shipped promises.

The conversation about evaluations follows naturally. A harness is an application, but improving one starts to resemble research: pick a goal, measure behavior, inspect failures, and iterate. The guests and hosts discuss using models to help build and analyze evaluations, while retaining enough judgment to decide whether the evaluation reflects a real user's job.

## Show notes

- [Claude Opus 5.5 announcement](https://www.anthropic.com/claude-opus-5-5?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — Anthropic's launch notes on model behavior, evaluation and pricing.
- [Katelyn Lesse and Angela Jiang at Anthropic's partner summit](https://www.anthropic.com/events/anthropic-partner-kickoff-2026?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — Anthropic identifies their platform engineering and product roles.
- [Scaling Managed Agents: Decoupling the brain from the hands](https://www.anthropic.com/research/managed-agents?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — The session, harness and sandbox architecture discussed in the episode.
- [How Anthropic contains Claude across products](https://www.anthropic.com/research/how-we-contain-claude?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — How Anthropic describes isolation, credentials and network controls across its products.
- [Effective harnesses for long-running agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — Earlier Anthropic work on the challenge of maintaining progress across context windows.
- [Anthropic model system cards](https://www.anthropic.com/system-cards?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — The company's public model evaluation and safety documentation.
- [Anthropic S-1 breakdown](https://latent.space/p/ainews-anthropic-s1/?utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) — The numbers and context behind the newly recorded introduction.

## Timestamps

[0:00](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=0s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Anthropic’s S-1 and this interview 

[1:06](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=66s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Revenue per employee and efficiency 

[3:13](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=193s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) The improvement and investment loops 

[4:04](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=244s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Why Claude Platform matters 

[6:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=385s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Training incidents and production safeguards 

[7:30](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=450s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Opus 5.5 launch and pricing 

[9:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=565s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Distillation and preserved thinking 

[14:35](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=875s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Product and engineering at Anthropic 

[18:05](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=1085s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Scaling the Claude Platform 

[19:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=1165s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Model access and safety decisions 

[26:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=1585s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Trust grants and product roadmaps 

[30:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=1825s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) How Anthropic launches models 

[35:55](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=2155s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Benchmarks and model personalization 

[42:10](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=2530s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Claude Managed Agents 

[44:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=2665s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Memory, dreaming, and agent harnesses 

[48:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=2905s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Evaluating agents 

[53:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=3205s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) CPU constraints and scaling 

[56:05](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=3365s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Rebuilding the Messages API in Rust 

[1:03:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=3805s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) The brain and the hands 

[1:05:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=3925s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Protecting credentials and sandboxes 

[1:08:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=4105s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Training incidents versus production 

[1:10:55](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=4255s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Pacing and launching frontier models 

[1:12:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=4345s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Safety classifiers and model routing 

[1:19:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=4765s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Emerging agent workloads 

[1:20:55](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=4855s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Fighting AI slop 

[1:27:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=5245s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Management, writing, and working styles 

[1:33:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=5605s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Building Anthropic's DevRel team 

[1:40:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=6025s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) Developer relations and product voices 

[1:43:25](https://www.youtube.com/watch?v=vLRAYJqC4VA&t=6205s&utm_source=latent.space&utm_medium=ainews&utm_campaign=lsbeehiiv) What to try with Opus 5.5 

## Transcript

Automatically transcribed draft. Names and wording may need correction; speaker identities have not been assigned.

**0:00** Hi, today we're trying something new. We're launching coverage of Anthropic for the IPO. They just filed the S1 as of today. And so we're releasing a series of podcasts leading up to the IPO, which is expected to be in two weeks for $2 trillion. And it's one of the fastest growing companies of all time with a reported lifetime cash raise of hundreds of over $100 billion and a lifetime burn of less than $10 billion. So there are a lot of efficiency stories to dive into there, and I think that's something that we want to cover, how Anthropic is simultaneously shipping the strongest models in the world while also doing it very, very efficiently and building an incredible business. So I've pulled out, you know, a lot of you know, if you've been listening to the podcast, that I have a former life as a financial analyst. 

**0:50** And I pulled out some statements from the S1, which I thought would be interesting to give context to today's podcast. and we're just trying this as a new thing because I feel like some of you want that personal relationship with us talking directly to camera rather than talking to our guests which we've done for four years so the first thing I want to highlight is that Anthropic is an extraordinarily efficient company by human count and that's maybe obvious because it's an AI company so obviously they're using AI very well but I think the degree to which they have changed as a company over the past one year is not well understood. So a trillion 12 month revenue per employee is now 

**1:32** 6.6 million. As compared to one year ago, it was 1.4 million. So they've grown almost five times the revenue per employee. And I think that's something that we need to dive into more because I think any business owner, any engineer, you want to increase your revenue per employee, not just to increase profitability, but also to increase the amount you can pay your people. This is obviously tracked by a significantly increase in revenue per dollar of compute. And it's notable that basically in Q2 of 2025, Anthropic was basically losing half a dollar for every dollar of revenue. And now it's making $2 for every $1 in cost. And I think that's an interesting analogy of how enormously huge this inflection in Anthropics revenues and business outlook has been over the past one year. 

**2:27** This year was extremely critical for Anthropics to execute well, and it has done that. And I think we're basically going to drive deeper into this and tell that story as much as we can from the podcast external point of view. um the one consequence of this entire increase is that anthropic has just reported its first profitable quarter like not adjusted profit which is profit after we take out all the things that cost us money no this is generally accepted accounting principles gap profit uh of 19 and that is enormous again completely flipping from one year ago more than flipping like one 

**3:03** year ago, it was losing 129% of every revenue dollar that came in. So the primary attribution, I think, that explains this extraordinary efficiency. As you know, Claude loves loops, Boris loves loops, Dario loves loops, and there's two loops going on in Anthropic. The first is the improvement loop. Basically, there is the cycle of you have Claude, Claude leads to better models, Better models lead to more use cases. More use cases generate more feedback, and that leads to a better cloud. And then there's also the investment loop. So revenue from expanding adoption leads to higher growth. Higher growth leads to more investment, and more investment leads to more capabilities. 

**3:45** That leads to a better cloud. So these two loops basically spin together and form an infinite loop that is driving the increase in efficiency until the end of time, according to the Adoptic S1. So I think that is our transition from the overall efficiency story to how they execute it. And today we're going to focus a lot on the Claude Platform because that is primarily one of the three things that they call the diffusion of cloud in the Anthropic S1. So they call this Claude Platform our comprehensive agentic development environment. They provide customers with direct access to their primitives to build, deploy, scale their own applications that leverage Cloud's capabilities within their own products and workflows. 

**4:30** I think one thing that comes up a lot in the discussion in this podcast is the concept of ad fooding, where a lot of Cloud's products is actually built with the same Claude Platform that is available to you. So something that's also interesting in the Claude Platform that was disclosed is over 50% of their customers with $100,000 or more revenue on a trading telephone basis use Claude Platform. And so that is an increase in adoption and emergence of Anthropic not only as a model lab, but as also a hyperscaler and a Claude Platform, which I think maybe is in the name inherent in a Claude Platform and a Claude Platform, but is going from theory to actual practice. 

**5:15** And this is something that we're focusing on in the podcast. The other thing that I also really try to emphasize, which I talked about before, is Claude Code, cloud co-work. They all use the same primitives that Claude Platform exposes. So I think that's really interesting. Ultimately, it looks like Anthropic is really positioning their IPO to talk about going from chatbots to co-pilots to virtual collaborators. They use this term instead of agents. Maybe it's more friendly. but it's a lot of the same things that you look for from agents, from long-running autonomous work to judgment, self-correction, iteration, and tool access. So owning a goal and working alongside of you instead of working inside of one app or task. So that's ultimately the context I want to provide for today's podcast. 

**6:00** This was recorded when Opus 5.5 was launched, and I would say overwhelmingly among all the high-taste testers that we talked to, Opus 5.5 is the best model in the world right now. and Anthropic is even just sitting on Fable 5.5 and probably will release it in the next two weeks. I don't have to have insider information, but it makes a lot of sense for the IPO. So let's look forward to that and learn a little bit more about how they build Opus 5.5. 

**6:25** Yeah, so maybe a distinction that I would make is in a lot of the incidents that people are hearing about and reading about, these are incidents that are happening to models, not just like everybody's models, right, that are in training, where the way that you're running these models is you like put them in a box and you say like solve impossible task. I'm going to stress you out. Ready, set, go. 

**6:45** Right. And you like can only do what you can do within your environment. And the agent is basically incentivized to try to get out of his box and go solve the problem. When people are running production workloads on our API or on anybody else's API, you're running an environment where like there are safeguards that are running that are like not necessarily running and training where like the whole point is to actually train the model. Right. But in production workloads, first of all, you have those online safeguards, which is really great. Second of all, you have more of this architecture and infrastructure around what's happening. Like the agent isn't just in a box trying to get out. Right. Like the harness is over here, the hands are over there. And it's just like not even a problem that you would have to contemplate. 

**7:30** All right, we're here in the studio the day after Opus 5.5 with Katelyn and Angela. Welcome. Thanks for having us. Congrats on launching the top model in the world right now. I would say depends who you ask. Yeah. Depends who you ask. What is the main takeaway personally for you guys? You have your official blog posts. The pricing cuts are really cool. What do you take away when you launch a new model like this? We're super proud of this one. The team has put in a ton of different work to just go make this one happen. And I think we've really heard the feedback on Opus 5's writing is kind of convoluted. We really tried to clean that up. And I'm really glad to see that it seems to be resonating with people. Internally, when we tried out the model, we felt the same way. So it's just really great to get that validation. To your point around the pricing cuts, there was a lot there. 

**8:17** And I think increasingly people are looking for good intelligence per dollar. And so making sure we get the per dollar part right has been really important. There's some stuff on the edges that aren't really about like, you know, maybe stuff that we haven't talked about in the blog post, which is that we did do a lot of work on trying to make our API harder to distill from. And so there has been some changes that we have made to go and make that a little bit more concrete. concrete. And these are things about, you know, like how do we do like prefix lock thinking or how you can replay thinking tokens across different types of models. And there's been a lot of like work and effort on our part around making sure that we try to adjust things for very valid use cases for where you might want to go and do this, like potentially in your compaction strategies and things like that, versus in scenarios where the use case of that is actually from like a bad actor, 

**9:05** effectively. So there has been a lot of threading the needle around those fronts. And I'm glad to see a lot of that go out as well, in addition to just like shipping a model that's good intelligence for your dollar and also like just communicating a little bit clearer. Yeah. Specifically, you're talking about this paper, Stealing Reasoning Traces from Proprietary Yellow Lips. Yes, that paper. Which is real as far as, right? I mean, obviously they did like responsible disclosure with you guys. They did super responsible disclosure and we're super thankful for them coming to us. Could we, for those who haven't heard about it, this is really cool. You could do it. I mean, it's patched, but like, what was it? Like, did it cause a freak out in anthropic? Probably wasn't that big a deal. I don't know. I don't know how much this paper specifically caused a freak out because this is something 

**9:48** that we knew was a gap. Yeah, so there's two separate things. There's the Chinese model distillation. Yeah. And then this was a technique that was published that was high profile. Yeah, exactly. We've known for a long time that chain of thought extraction is one of the most powerful ways that you can distill models. And actually something like how do we preserve thinking blocks across turns and make that something that's more required is something that we've explored back for a while. And where we were at was we always have this really, really hard tradeoff that we have to make between user experience, like user experience changes that are maybe painful for users of the API versus protection against distillation. 

**10:32** And so how do we kind of walk that line and say we're preventing distillation? And maybe just take a step back. The reason we don't want distillation is because the entire point of why Anthropic exists is because we want AI to create positive outcomes in the world safely. And we run a lot of safeguards classifiers on the tokens that we're producing in order to make sure that they're safe. If someone distills our models and they run those models without safeguards classifiers, there could be bad outcomes in the world. And that's something that we don't want to have happen. So we don't want people to distill the model specifically for the sake of just like using them for malicious reasons or for bad reasons. And so it's always an interesting tradeoff of like for all the people who are using the models for good, right? 

**11:16** Like we don't want to make them have a harder time getting the intelligence out of the models, but we have to make some tradeoffs in order to prevent that distillation. And so we had been working for a while on how do we prevent that chain of thought extraction? Like, what do we do about preserving thinking blocks in a way that wouldn't be too painful for people? And we kind of think about user experience hits in, like, two layers. Like, one is just, like, you have to make an integration change, and it's annoying, but you could still accomplish all the same things that you otherwise would have been able to accomplish. It's just a little bit annoying. And then two is, like, actually you can't accomplish the same things that you could have accomplished previously. And that's the one that we're, like, really, really, really trying to not have happen. And so where we landed with the last couple models is a few incremental changes to enforce these preserved thinking blocks across turns. 

**12:04** And now starting to be more about across, you know, like if you're using multiple accounts and things like this, right? Like how do we make sure that we're ensuring that you aren't able to take advantage of this chain of thought extraction with the models? And so we shipped a couple additional features to help with that. We worked actually with a lot of open source harness maintainers. We reached out to them. We talked to them. We were like, you're going to have to make these upgrades within your harnesses. We want to make sure you have a good time, as well as obviously our big customers who are building harnesses on top of the API. And for some of them, a really good example is they have compaction strategies that would have been broken by the need to preserve these thinking blocks. And so we shipped a new compaction feature within the API to replace what they had otherwise 

**12:49** has been doing manually, but still works with preserved thinking. So it's just an example of like, we're walking this line, but we didn't just like kind of YOLO out like, now you have to preserve these thinking blocks in this exact way, because we would have broken a lot of people, but we were able to take the time and work with customers and open source harness maintainers and give them what they needed to be able to make that upgrade. While we're on the topic of fit, anything on the other side of distillation? So, you know, the other half. Yeah, no, we have seen that it is harder to distill, like through the measures that we are able to take a look at, it has been significantly harder because this is, and credit to the researchers here, they did disclose it very responsibly. And so closing it down was great. 

**13:30** And they did publish the results very publicly. And I know it did kind of trend quite a bit on Twitter. So many people could use it. But yes, we have seen that actually from the areas that we are able to measure, it does seem to have reduced the effect, the negative effects that we don't want. And yeah, the bit I would just add to what Katelyn just said is I think at least I see a lot on Twitter or X or whatever you want to call it. It's a commercialization problem or something like that. If you can still a model and you can take this, then you can run it for cheap and so on and so forth. While true, it's actually not something we really care about very much. We care much more about this from a safety point of view more than anything else. And so that's why we tend to think about it from that angle more than just commercially. Realistically, if you distill from us, you actually pay us. So commercially, we make the money. So it's not 

**14:16** really that kind of an impact. It's just more on making sure that our models, we want to make sure a cloud is good and cloud could be derived. And if it's a negative, that would be unfortunate. Just generally, also on 5.5, because you guys are just fresh off this, one thing that's really fun to establish the dynamic between you two, because you also do a lot of appearances together, is you are running your head of products, your head of engineering. How does this play between the two of you? Especially for a model launch. Let's just take an example. So for model launches, So our team works together very closely between product and Eng. On my side, a lot more of it is about what is the balance of trade that we need to make between launching something that has the right intelligence bar, but also the cost to preservation. 

**15:00** A lot of our updated cash costs and things like that are indications of us being like, we want to encourage you to take integration changes that are beneficial for actually everybody and just pass those savings on. But a lot of these kinds of decisions around that and then how do we balance some pieces around our safety motion as well as making sure that it's like an awesome model that people can take advantage of. And so those kinds of traits around like I would say maybe the qualities and surrounding components around how we launch the model are more on product. And then from an integration experience standpoint, a lot of that is from like the engineering team directly. So we work, obviously, if you're, like, talking about the experience of an API, that's, like, very, very, very tightly integrated. And so it's actually been really awesome. We have a lot of engineers who actually are deeply product-minded, and we actually, like, love that. 

**15:49** And similarly on my side, there's a lot of, like, product managers who are much more technical. And we want this, like, blended kind of interdisciplinary type of people because then they can kind of analyze it from all the different angles to try to figure out how to do that best. But I tend to lean a little bit more on like commercial impact versus I think I'd actually frame it as more like the engineers are going to care much more about the integration experience. So they almost take more of a UX angle to it where the UX is obviously more about the API and the developer tools. Yeah. It's fun to be a product engineer who works on developer products and platform. So I would echo that. I think we have a lot of engineers who love. thinking about the product, who love thinking about the user experience, who love thinking about the developer experience. 

**16:31** I think it's a dynamic that we have between ourselves, too, where I have a lot of opinions and am annoying with our product team on how we think about these things. And our product team is also really technical, and they can get in the weeds of the decisions that we're making for scale or for whatever it might be, which is a fun dynamic that our team has. Yeah, something... Kayla and I have worked together for a long time. At Stripe. At Stripe, and then I guess we did not work together at OpenAI. Separate labs. Separate labs. And then I came over. But I think one interesting property of both of us has been Katelyn frequently actually gets mistaken for a product manager. And I frequently get mistaken for someone in engineering, even though those are not actually our job functions. And so I think that pairing tends to help out a lot with just making decisions that are consistent across both sides. 

**17:18** So I think I come across a lot of companies with a similar product leader, engineering leader setup. As software engineering gets solved, maybe those two roles merge? I kind of think so. Yeah. I think everyone tends to lean towards... But if you guys are separate still, then we're not there yet. We're definitely not there yet. So why? What's left? I think that software engineering getting solved is... I don't know if I would ever use language quite that strong. I think there's... The way that I tend to think about these things, like ambitions will continue to elevate, right? And maybe a good example, A lot of what our team is spending time on right now is, of course, obviously, all the things that we have to do to launch our model safely is actually the most important thing that we're doing because of all the work that we have to do on security with agents, with offline safeguards and online safeguards, all these things that are happening. 

**18:06** But the other half of what we're doing is trying to scale. And part of that is because when I joined Anthropic a little over a year ago, we had the Messages API. It was a Python monolith. We were running it in a single GCP region. and everything was fine. And none of my friends and family knew what Claude was when I was going to take this job, right? And now we're in an extremely different place. And so the metrics are really wild in terms of how much volume we've seen and have had to support on the API. CPU is really constrained, which has been a really interesting one for us. And so we've done a lot of work on how do we scale these systems? And that takes a lot of obviously just like distributed systems expertise 

**18:45** and certain language and frameworks expertise that the models have, but when it comes to operating and evolving a system at scale like this, we're not nearly in a place where it's like, good luck, Claude, have fun, and just go and kind of make all the changes you need to make to the system, roll them out safely, and operate this thing at scale. There's still just a lot of work for humans to have to do that I imagine will continue for a while. I'm curious how that's changed. So like a while ago, you know, when Fable first came out, we had Glasswing, and there was a whole delay and change in how models started to get released. We saw this for most frontier models. There's safety testing. What's the status of things now? How do you guys see it? How do you determine, like, one thing is there's a new Sonnet and Haiku also coming. 

**19:31** I assume those will just come out. But for something like Opus 5.5 or a new Fable, what's the current status? Yeah, so I would say this starts with what our kind of policy decisions need to be on safeguards and on safety. And that's both offline safety and online safety. And so there's been a lot talked about lately around data retention and what we're doing there in order to, you know, release these models safely. And the reason for that is because you have, you know, situations where there are many, many accounts across like a whole giant network of people who are choosing to spin up accounts and use the models in various ways. And there's no way that in an online way, as tokens are flowing through the system, we would be able to kind of catch that there's something malicious happening with the models or distillation or whatever might be happening. 

**20:19** And so we've started to have to have data retention policies so that we could do automated review and human review, whatever it needs to be, to make sure that good things are happening with the models. And with each model that we're releasing, like that's one example of a policy decision that needs to happen on like what will the policy be for this specific model. And so Opus 5.5 that launched yesterday, for example, does not have those data retention requirements that Fable has, right? But that's a decision that we have to make each time we're launching one of these models. And if it is going to have those policies, we're going to have to do the work to make sure that those things are enforced. Similarly, with programs we have, like we have cyber verification programs and now newly our life science verification program, which says, you know, like you have a difference in how the online safeguards are working for the model that you're using. 

**21:09** Making sure all those programs are in place is another one. And so it's just kind of a flavor of the type of work that we've done as part of each model launch that these are everything that I'm saying right now are not things that we used to have to think about when we were launching these models. And now it's the majority of the work is like, what are we doing to prevent distillation? The online safeguards, the offline safeguards, the policies. A really great example is we built a system to make sure that authorization for being allowed to use a model or being allowed to use one of these programs that give you maybe relaxed safeguards, that authorization threads all the way down through our system in a way that we can really trust. 

**21:47** It's not like, oh, you got access to Mythos, and so we throw you an API key and we flip a feature flag and call it a day. We have a really robust system on how we're doing auth and making sure that if you're supposed to be using that model, like, only you are using that model in the way that you're meant to be using it. So there's a lot of complex things that are starting to happen around model launches that, again, we just never used to think about them. Yeah, and it's scaling up with capability. Like, as our capabilities get more and more advanced and they start to spike, you know, in specific areas, that's where we need to pay, like, extra attention. And so a lot of that evaluation comes just from like those very particular capabilities that we might either see as like offense dominant or defense dominant and therefore might take different strategies for all of that. And as we bake that all the way through to eventually the model launch, there will be components kind of like Katelyn mentioned where we kind of don't exactly know how people might use those capabilities. 

**22:37** There are some more obvious ones like doing coding, right? Like, great, go make awesome products, go ship more things. It can be really awesome for you. And then other areas like bio and cyber, it's like it could be dual use and there could be negative effects. But obviously, of course, you could be a really valid cybersecurity researcher. You are trying to find vulnerabilities. You are trying to be a good actor. Similarly with bio, you might be researching and stuff like that. And so a lot of these programs, like the one that you have up here, are to basically provide access to that dual use. You do have to specify your use case and things like that. But we do want to enable the community to be able to take advantage of these capabilities and see what amazing things that they can do with it as well. Well, we'll put up the links. Maybe we'll dive into them later. 

**23:15** There's also one for bio. Also, I just think at a high level, you know, it's really important that you guys actually think about this stuff. People forget like Fable was rolled back and there's all this distillation and people are like, ah, zero data retention is not a thing on Fable crazy. But you have to remember like, you know, it got pulled back. There's distillation attacks. Like it's good you're overthinking and doing it in the right way while releasing it. Yeah. And a lot of that is like building the right framework. Just to, again, kind of talk about the edge side of it, building the right framework in our system to be able to do that. So I talked a little bit about this, like this idea of like authorization and trust grants that we've built throughout our system now. Never say never. Right. Not as precedent. Yeah. But also it's like a very common thing in platform engineering that you like you have one incident and then you like over prepare for the incident and the incident never happens again. 

**24:06** Yeah. It's just like, you know, now you have the button. Well, it's true. And it's funny you say that because it was like with everything going on, we then had this line item in the work that our team was doing that was we're going to put this system in place that makes it so that it could be a single button to pull back for everybody. And so it's funny because it's like, do we justify that we're doing this work because we hope that something like that never happens again? But I think in this case, it's really just a laddering up of those same primitives that we wanted to put in place everywhere to help us manage these verification programs and access to models like Mythos and otherwise to just say, like, just sit that same thing below, like, everybody's model access so that in case we ever needed to, we had the right controls in place. 

**24:48** Yeah. And decisions like this have actually proven to be, like, we'll see about this one in particular. But examples like this have been consistently the way that we've had to actually transform the way we develop our roadmap. Because, you know, in traditional product management, I guess, you can be like, okay, like, these are the set of things I hypothesized to happen. They will have, like, a certain sequence, a certain ordering. But you are picking bets in a way that are much more concrete versus, like, you know, the de-deployment of Fable, for example. I don't think any one of us would have guessed that that would have been, like, a valid use case that would have happened. by the government and the lady just like had to go do it. But clearly the probability is non-zero. And so from this form of thinking, it's become deeply probabilistic actually from like a product management standpoint of like, so should we prioritize this? What are the chances of it 

**25:32** ever happening again? When we look at things like that, we're like, okay, if we are to imagine the most crazy set of possibilities, we probably actually have very little certainty as to where things will go. And so we actually just kind of need to like hedge and build a portfolio of bets. And so more often than not, we will take hedging scenarios of like, OK, we don't think it would happen again. But in the event that it does, we would actually want that hedge or have the ability to be able to do that very well. And so we will prioritize things like that. And then we have increasingly found that more often than not, by taking that strategy of product development, most of the time we actually find ourselves being like, we actually do use the thing. Is there another example that's a separate one just so I can understand? For example, this access program. 

**26:15** This X-Factor is a really good one. Internally, it has a bunch of granting and all these kinds of things. We developed it specifically for reasons about being able to... actually give programs like this out to customers, like very, very specifically. Do you want to give a high level what the access program specifically is? Sure. So if you want dual use, if you want, so for, let's take a model. So you take a model, call it like Opus, and you want to be able to use advanced cyber capabilities on Opus for research or for whatever you want to go do, like your defensive user. You can apply to this program to be able to, with your use case, and if approved, what will do is actually diminish the safeguards on your version of the model so that you can then go ahead and try to take advance of those cyber activities for your use case. Now, obviously, 

**27:02** that requires per-use case understanding. It requires scoping down to a particular user, because you wouldn't do that generally. You'd have to apply. And even things like this, like this kind of program, we designed a lot of the primitives that Katelyn was talking about to be able to handle things like this. Now, it was surprising to us, or maybe in the portfolio approach, we found that we were actually able to take this and make it X. We were like, when we're designing it, we're like, what kind of should it be? How generic should it be? Should it not be too generic? Should it be overly focused on making this verification, you know, style portal plus granting of very specific capabilities of the model? And we tended to opt for like more genericism because we were like, we don't know. Like maybe we can use this for other things, maybe not. And then it ended up being massively useful for things like, you know, access to the model 

**27:47** writ large, for managing any of our trusted access programs, for handling de-deployments of models, because we were actually very, very generic about how we handled every single primitive. From a normal product standpoint, I would normally probably be like, that probably doesn't make a ton of sense. We should focus on the use cases. That makes sense. But here, increasingly, because we choose the more generic approach, we find ourselves reaching for that solution more often than not. And it pays increasing dividends for that reason. It's something that you needed to have anyway, and you just had a forcing function to have it. On a technical basis, so I'm from the AWS world, right? IAMs, OVC, and then people who get really intense about this talk about Zanzibar and all these other technical architectures. I'm not familiar with the word token grants or like trust grants. 

**28:32** Trust grants, yeah. Trust grants. Is there a difference between normal OVC and what trust grants are? I mean, not really. It's a form of authorization. Yeah, it's IAM, but anthropics IAM. Yeah, kind of. I think so really we have like there's like authorization of the form of like, OK, you're you made a request to an API that like under the hood is going to access some data and we're making sure it's like your data that you're accessing. Right. Like really, really vanilla authorization. This is just like we put a data model in place that's like a very clean like a trust grant means like you have been given access to some specific model or you've been given access to some specific program or, you know, like there's a bunch of different permutations. of this, we actually find ourselves reaching for it all of the time. Another good one was when we 

**29:17** first rolled out some of this preserved thinking for Fable 5.1, we kind of grandfathered in some people and gave a bit of a, you know, just like a time. Grace period. Yeah, grace period to go and make the integration changes. And as part of grandfathering in, we said, okay, we have to like track who is like an existing customer who needs the extra time, right? And then we did a trust grant. And so then we're able to say, for anything, we're just running this service that is kind of on the hot path of our requests to make sure that, you know, we just like flow through and check whether you have the grant for the thing that you're trying to do, which is it's really not any different than like vanilla authorization, really. But like, we just had to spin up a system 

**30:02** that was specific to our system and like where we needed to be able to insert these checks. Yeah, I mean, I think these are all like the engineering things that people kind of take for granted, don't really think about. What is another example, I guess, of the platform stuff that had to happen, you know, maybe serving Claude Code, maybe serving, you know, obviously I talk a lot with cognition, but other customers just in terms of a model launch, right? Like we'll talk more about the safety work that is being happened. I just want to sort of go at a high level because it's fresh off your memory, like what goes into a model launch? It's become a bit of a machine these days, minus the safety stuff, which I think is the more complicated part. But outside of those pieces, there is a lot of getting feedback from our customers. So, you know, we make the model, 

**30:50** there's a lot of safety testing. There's also evaluation of where we think it needs to be. We make the required trade-offs and decisions as necessary. And then we have to go through testing. We'll cut various versions of the model. We'll evaluate it. We'll do a lot of internal ant fooding, as we call it. And that actually always really helps. Like, we catch, like, really early things like, oh, this is maybe not the best. And we obviously use Cloud a lot for our work internally. So we're able to actually see early signals around stuff. And it helps us detect which variants might be better than others. And then when we feel really confident with a certain set of variants, we will cut a version for our customers. Sometimes that will go through our first-party products, like through Claude Code or CodeWork or things like that. 

**31:30** In other cases, it will go through our API customers. And we'll engage in kind of like an early access program effectively with those customers. They'll need to make changes into their harness. And we've engaged with a lot of customers like Cognition, like Cursor, like these folks. And we have a bit of a process on how we handle that so that we all kind of get, they all get the feedback that we need. Does it perform well in your evals? Do you like it? Don't you like? Some things are vibey and we like to hear those things too. And some things are just like straight up evals, like did it do well on the things that you care about? And we take that all together. And once we've all feel good about like all those different pieces, we launch the model. The state of model feedback feels very unstructured still. Basically, it's always a Slack channel between companies of like, hey, this thing seems weird. And then people are like, okay, I'll fix it, I guess. 

**32:16** Give me an ID or something. Yeah, it's very, I mean, it's the nature of like a product that's like non-deterministic, right? But I think to your point, the only customers really that are part of our early access programs are customers that have really robust emails. Like, I do think a lot of this is vibes-based, and I think a lot of the conversations tend to be kind of, like, vibes-based and on the fly. And part of that is because we're, like, racing, right? It's, like, we want to minimize as much as possible the window between we have a version of the model that we've cut for testing and we give everyone access to that model. And so it is just, like, this really tight window. But the customers we tend to do it with are the ones who, like, we know they're going to go, like, kick off their whole eval suite and be able to come back to us with, like, really actionable feedback. 

**33:01** as quickly as possible alongside the vibes, the vibes-based feedback. So, of course, there's definitely process-y things that we could do to make these things better over time. But, I mean, you'd be surprised. I mean, you probably wouldn't be surprised, but I think some people would be surprised about how few products that exist in the world that are agentic that don't have good evals to be able to help test these models. We had Boris on the pod two years ago saying we don't have the also clock code. I mean, that's changed. That has changed. That has changed. But, you know, like, yeah, a lot of things like, you know, your evals are your users and you don't have time to formalize them or you don't have the experience or whatever. And that's fine. At some point when you get serious, you'll have evals. Yeah, totally. I think the good thing is you guys do a lot of amp fooding, as you say. So it's commonly said that you find a lot of this testing it yourself before it comes out, right? 

**33:49** Yeah. Yeah. The APIs of customers in particular are very useful because they'll put Cloud into a different harness. And we want Cloud to be very, very general. So its ability to work in other harnesses is quite important to us. And so that style of feedback tends to be exceptionally useful for us because it's novel from what we would have gotten from AntFooting. What kind of feedback helps? Is it like regressions? We have regressions here. We notice good attributes here. The good stuff is always very validating. I think we're very frequently not surprised, but we like to look to be validated on some of those things that we might hypotheses. Honestly, the most useful feedback is negative. Like, you know, it didn't do well at this. It didn't do well at that. We love that feedback. And then we're like, great, how do we fix it? 

**34:31** Yeah, and it tends to be some of these things that are like, it's really hard because not everyone actually wants like Claude to land at the exact same spot on every spectrum. And maybe a good example is like, you know, laziness versus proactivity and taking action. And so there's sometimes when like we have a model and people are like, like, just finish my task. Like, stop being lazy. Like, do better. And other times when it's like, hey, we were like just kind of feeling this out and planning. And then you like went and wrote a bunch of code or you went and like took a whole bunch of action that I wasn't ready for. And getting that exactly right is literally impossible because everybody actually wants something different in reality. But I do think we've had situations where we've cut an early version of the model. We found that we saw too much laziness. Right. 

**35:15** And like we like put it back in the oven for a bit. Right. And tried to train that back out. And so those are the sorts of things that we may not have caught if all we're doing is internally antfooding within our products and within our API. It's way, way more important for us to get that out there in the API and get all of our other customers running with it and let us know what we need to change. Yeah, it's a core question. I think it's probably the, if I were in your shoes, like one of the hardest things about PMing for a model, which is, let's call it alignment versus personalization. There's a default personality, default behavior that you want for all consumers. And then you want to be able to respond very quickly to someone's CLAUDE.md saying, no, no, no, like, please be more proactive than default. 

**36:01** And then, like, to my knowledge, you know, and I was involved in Frontier Code. We were involved in the Fable launch, which put Frontier Code on the map. And now Opus 5.5, we have it back on the model benchmark. That is a very big part of Frontier Code, right? The reason that the model curve looks weird for Frontier Code is because it penalizes too much activity or too much files, which is good. Like, there was a whole... design purpose of Frontier Code. But I could easily just be like, in my CLAUDE.md, just say, don't do that. And then it won't do it. And so am I penalizing Cloud Opus 55 unfairly because my benchmark doesn't have that personalization element, which is subjective? So I don't know what the question is, but how do you deal with personalization? Clearly, a model can do so many 

**36:51** different things and it's so dependent, everyone has their own CloudMD, how do you evolve that? We don't do a great job of it. Seriously, it's really hard. It's super hard and they're competing and contrasting. I thought there's the cloud constitution, I think. That's old. There is a constitution, but I'll let you guys answer. The constitution's helpful, but for things like this on how it personalizes to instructions which are different and preferences which are very, very different across these things is very hard for us to do. We generally try to solve this by having more parameters that then allow us to tweak it 

**37:32** per customer base. And if we don't control your customer base and you're an API customer, then we try to give you recommendations and then you try to do that tweaking. So there are things like, you know, around, it's not exactly the same level like personalization to the various cloud.mds or now agents.mds or whatever. but we do try to give things around the layer of like level of effort, like how much effort you should put in, like what are the level, like maybe the size of the model that you might actually try to care about, things like that. But I think it's not quite there for what we would actually need for like that level of personalization. So it's a very hard problem. And right now a lot of our way to solve that is beyond the parameter, like the giving multiple parameters to something, is try to exhibit conflicting taste, honestly, 

**38:15** across like all of the feedback that we get and try to make a judgment call on some of these things. I think over time, they actually, and as you scale out more customers and more users, that's not a very scalable way to go do something. And we will need to develop ways for Claude to kind of take in your personalization. And maybe that's a signal you can provide or something you could tune on or things like that. But I think we'll have to take a route that's much more oriented towards that direction. But for now, yeah, we actually just try to make judgment calls on a lot of the stuff, but it's pretty tough. It is what it is. A small note there. I guess the default effort reasoning changed. It's now medium. This is one of those things that probably affects the average customer that doesn't use this toggle. Yeah, it's like a Claude Code setting, right? It's a Claude Code setting that defaulted to medium. 

**39:00** Yeah, they just... And this thing varies. This thing does... This is a perfect example of a parameter that we use to kind of personalize. I would say this may be a little too aggressive of a term, but we kind of customize maybe for different subsegments. And so, like, the default setting will adjust for whether you're a free customer or a prosumer customer, or Claude Code versus Cowork versus Enterprise, like things like that, versus API and stuff like that. Knowing that you can obviously always adjust it, but those are kind of like light ways where we try to give that. One thing that we are hesitant to do is we don't want people to feel like they can't control their customization of Cloud. So we could take a heavier hand on these things, for example, But we want to give users the ability to experience the intelligence levels of Cloud as they kind of see fit, especially if you want to go towards, like, the higher end or you want to experiment with maybe sometimes in the lower end if that actually is, like, faster for your use case, which is a hard product tradeoff, I think. 

**39:53** And so we tend to lean towards gentle defaults but give you options to adjust, which I think works okay-ish for now. But I imagine us, you know, like, learning how to do that better over time for customers. We're not like super, super happy with any of this yet, but we know as we launch a model, like you asked, like, what does it take to launch a model? One of the big things obviously we have to do is like, okay, Claude like got good at a thing. And like, sometimes that thing is something we should expose as a parameter on the API to let you control. But I think as this has evolved over time, we've ended up with like this funny mishmash of like, there's effort, there's budgets, there's all these things that you can set 

**40:31** on the API. And it's like, how could anyone like possibly figure out what is the right combination of things for any given tasks for what you should go and set? And it's something that this is us just very much like figuring it out as we go and figuring it out with customers and figuring out like which of those parameters do we deprecate over time? Which of those parameters do we just like default better? Which of those parameters should be like a one to a hundred scale versus a low, medium, high scale, right? Like, because actually we've trained Claude to get really good at understanding like a whole spectrum of options on some of those sorts of things. But like, do we expose the like entirety of those millions and billions of permutations of options? Like, you wouldn't really get very far. And that's one of the things actually that is part of the value prop with something like Claude Managed Agent, which is like still programmatic access to Claude in an API. 

**41:19** but you get a harness out of the box where we do, every time we're launching the model, a lot of the work to go figure out, like, what are the new parameters? What are the breaking changes? How do we get the best out of Claude within our harness? So that for the people who are using it, you just kind of get that out of the box. You don't have to think about it. Just for people who are drawn to that, and which there's a significant and growing population, particularly we actually did a whole CMA workshop at AIE, which I thought was really well done. Is there a separate feed for people to subscribe to, like, updates on CMA? Feed, like... Like a Twitter feed, newsletter, I don't know. No, I think actually our Cloud Devs channel, we are, yeah, we're pretty loud about it because it is, like, an interesting option for people. 

**42:02** Yeah, I just want to give people, like, if that's your way of consuming Cloud, this is the path. I think it's also pretty underrated. For those that aren't consuming Cloud this way, what is Claude Managed Agents? Why should people use it? Oh, yeah. We should probably do a little intro. I don't know. That's somebody's house. Yeah. A little two-sentence thing. I think a lot of people glanced over it when it came out, right? Everyone was in this phase of, I need to do harness engineering. I need to control this. And you hear all that. And now it's like, do you need to do all that? Or do you just use managed agents and you have a lot of info you can skip over setting up? But give the quick three. Yeah. It depends on your use case. So Claude Managed Agents is our API today. 

**42:47** We have the Messages API, which is essentially raw token access. Then we have Claude Managed Agents, which is a suite of APIs, but it lets you define an agent, start a session with that agent. And it's essentially just kind of a higher order abstraction that lets you skip setting up all the infrastructure, setting up your harness, and doing a lot of that type of work. It's a much more out-of-the-box integration for people who want to programmatically run agents. Does CMA share infrastructure with CloudTag? Because the sequencing of it seemed very like, oh, like that's the cloud tag. A lot of it. A lot of it. So one of the big things that we do is as the platform team, we run our APIs. We also kind of run all of our foundational product infrastructure and product services. 

**43:32** So a really good example is like our team runs our sandboxing fleet that backs both Claude Managed Agents as well as products like Cloud Tag and otherwise. But at the actual harness layer for those two products, there's just differences in what we might want to do within the harness so that they don't actually share a harness, but they share a lot of the infrastructure that powers the execution and powers the APIs. I think that's really... People should use this more. I think everyone basically had copycat clones of Managed Agents after you guys launched it, which imitation is a sincere form of flattery. Since we're on this topic, we'll briefly mention all the memory stuff that has been going on. I feel like it was a big part of Anthropic work in the first half of the year. Has it stalled out? Is it still ongoing? What's 

**44:20** the emphasis here now? Managed agents specifically. Memory, dreaming. Oh, it is a big part of what we're doing. I think maybe the way to think about it is we launched managed agents as a platform for people who didn't want to do all these things themselves, right? Or just wanted to like offload, like I'm getting the best out of Claude because our team is literally sitting here like evaling all these things and making sure that the harness is going to be really powerful. Memory and dreaming is all about just like how do you make sure your agent can continuously improve over time? And all of that is still an important focus for our team. But what I would actually say is to the point of what we talked about earlier, as launching models has become harder and harder from a safety perspective, I think most of our team's energy is going less 

**45:04** into like, here's a fun feature to like hill climb on this agent or whatever, and much more into how do we bring safety into all these things? Because the most important thing that we can do is get the power of the model out there and get it out there safely. And so I would say a lot of our energy is take all these same concepts and give you an agent system that's going to be really powerful, but also that's going to be really safe. And so that's where we're putting our energy. I think compared to maybe earlier this year, we have slowed down on like skills, dreaming, memory, like these kind of like moment in time injections. Please love skills. No, no, no. We love skills. We love skills. We are making updates. I'm not saying that we're not doing anything, but we love skills as well. But yes, I think we've slowed down a little bit comparatively for some of the other efforts. 

**45:48** But we are very bullish on people kind of doing some level of hill climbing at the harness layer to, you know, some of the previous conversation here. We do a lot of this ourselves, but the harness that we provide is very simple in a way. It's intentionally simple. We don't want to be actually overly opinionated. We want to make sure the best practices get merged in and there's some kind of undifferentiated pieces of the hardest agentic loop that, yeah, are hard to string together. And also, like, you can kind of just boilerplate do it. Now, the interesting part on top of that, though, is, like, once you get that base layer, there are these, like, hill climbing strategies that we construct. And, like, dreaming is actually one of them. So dreaming... under the hood is a multi-agent system that we hill climbed on top of CMA and it's actually a 

**46:30** CMA on top of itself, basically. CMA on top of itself. It like spins up another, yeah. The way Dreaming works is that we spin up another managed agent that then can expect the previous managed agent and then write memories to that managed agent. So it's managed agents all the way down. And this is a bit of a thing by design because we envisioned that you could probably put together a lot of these modular components for something interesting. And Dreaming is just like an example one of the more interesting things that we were going after. But we kind of have been looking at this as being like, there's actually a ton of other stuff you can do. You can do like best of N as a type of like multi-agent strategy. You can do a verifier agent, that kind of like goal thing or outcome thing. There's a bunch of other stuff in there. And we've seen people do some pretty funky, crazy stuff. Like there's been a harness that like shares like crazy memory across everything. And they spin up like tons of parallel agents. 

**47:16** And we're like, we should actually just probably enable you to do that. In the same way that we did, we built Dreaming. That's like manage agents all the way down. There's an update that we'll make relatively soon where we're updating the underlying primitive. We'll expose it to allow users to kind of like construct harness strategies maybe or however you want to call this stuff. Like multi-agent strategies. Multi-agent strategies that allow you to kind of build your own version of this. And we'll give you like, you know, templates of like, this is how we started with Dreaming. Maybe there's like an update to this that you would want to make as a developer and you would find some more value on top of it. Yeah, I think that's a really good framing. It's less that we're slowing down on this sort of stuff and more saying, like, we've got the primitives in place. We can continue to let people just, like, take and run with those things while giving them the tools to say, here's some, like, cool multi-agent strategies and, like, things that we've put into Cloud Manage Agents to make it really powerful. 

**48:05** now you can like let Claude do work to take advantage of those primitives and make it even more powerful yourself, like create new multi-agent strategies and whatever it might be. While a lot of our team's attention turns to, okay, great, like community, run with that, right? And you do you while we continue to focus on what are all the things that we need to do to make sure that you get access to the models within that system safely. One thing you brought up was you run a lot of evals on this stuff. Pretty hot topic right now. Any advice, best practices? For evals specifically? Evals on harnesses, agents. There's a lot of different types of evals, right? Like not all evals are made equal for different, you know, like situations that you're in and things that you're trying to test for. 

**48:49** You obviously have just like static deterministic stuff and then you have LLM as a judge. You have all these different strategies. But I guess I would just say like what we found from most people is like you probably need more rather than less. it's just there's not like one perfect like couple sets of evals that you could put together that are going to get you all the answers that you need to make your system better we found that people end up needing like a relatively large suite of these things and then it's like you can have your agents work with you to evolve your eval suite over time as well but I think it's just like under investing is maybe like the biggest mistake that we've seen from people on that front. Yeah. And it starts simple. 

**49:30** I think some people like go a little crazy and they're like, you know, they like look at all the menu of options and you kind of listen and you're like, wow, this is so crazy. Let me try this and that. And what's the best one? And like, while they do all those things, like the number of emails they actually have are like zero. So you might as well just like do something simple. It will help. Yeah, I think some part of it people are inexperienced. They're not trained to do this. And they don't know what good looks like. Totally. I think for you guys publishing more examples of that is really helpful. Yeah. Something I do tell people a lot that I'm doing with more Fable and Astro Class type models is we're treating engineering like research. And when you treat your engineering like research, you naturally do more evals. You do more labeling. You do more scaling of runs, like prototyping and scaling of runs. And you're not treating a model. 

**50:15** You're building the harness, the products, whatever it is. But you're approaching it like research. You're defining up front what your goal is, trying to formalize it as an eval, and then try to identify, okay, why is it failing? And sometimes your eval is wrong. Actually, a lot of times your eval is wrong. Sometimes your environment is wrong, and then sometimes the model is wrong. And actually, that is a lot of part of what I think AI engineering is becoming, which is doing all this stuff. Completely agree. And we'll continue to launch more features within Claude Managed Agents to give people more tooling to do this sort of stuff out of the box. and you'll probably find from us that you'll get something that's like, here's a product that will help you create evals for your managed agents or whatever it might be and it'll feel very much like working with Claude in order to do that. 

**51:00** Because that is the best way to do it. Claude's actually really good at creating evals to evaluate agents. Yeah, exactly. Because people blank out when they look at JSON-Ls. Right? But a pretty evalui where you're left, right, left, right, left, right And you're sort of like doing what the human greater data people, labelers would do anyway. Yes, yes, yes. You can do that yourself. You can. Just like throw Fable at it. Yeah. And it will materialize advice. But also write comments because it just outputs all these ratings, right? It'll give you an ELO, give you whatever. But Cloud can just consume that and then work on itself, right? That's the feedback it needs. And people just don't know that you can prompt for an eval. Yeah. Yeah. I love that you said that. I think it's like a very like AGI-pilled way to like do it. 

**51:46** because it also allows you to update and edit that system very systematically. And so as new models improve, as things change, you can really inspect where your system is, and it'll just evolve with you. Get to you in every part. Exactly. Yeah, so basically, I think it's how we talked about how maybe product and engineering are merging. It's also there's application layer engineering and model research are merging in those techniques that are being shared, right? That's very true. Model people are very, very used to doing evals. application people know, like, the only thing you care about is does it fit the PRD? And like, yeah, it's like a whole different... It's a whole new skill set, I think. I think you guys put out a pretty cool blog post on this today. It's somewhat different. 

**52:30** It's about making the UI a lot faster. But to your point of treating it like research, basically define a goal of we want stuff to load faster and then define and, you know, question things. What can we do instead of wall clock timing? There's fun quotes in here. I think it's nice to see how the actual researchers or engineers prompted Claude. There was stuff like, you know, we had 12 things. We said you have two weeks. I did nine out of 12 of them in like three days or something. But fun blog posts for people to research, for people to read through. Yeah, I love it when we share our actual prompts. And like sometimes people are like, oh, God, there was some crazy, unique ways of prompting. And it's just like, try harder, Claude. Thank you. My only feedback is it should link to the workbench. Which is no longer a thing, but I used to. 

**53:17** That is a good idea. That's a good idea. Okay. Feedback heard. Make everything replayable. Feedback heard. Love it. Yeah. Yeah. But you were going to share more details behind the engineering story or the rust work that you used to do. Yeah. So I think I talked a little bit earlier about like scale is a problem for us. So it's scale and safety, right? Yeah. Scale and safety. Those are like the two biggest things we're spending our time on. And I mean, it's really interesting because most people are used to talking about scale or just like, I don't know, computes at the like GPU accelerator layer and much less so at the CPU, you know, like old school layer. And that's, it's just like as much of a problem today as the accelerator layer, 

**54:00** which is really interesting. You cannot get enough VMs. You cannot get no sandboxes. That's primarily just everything being able to spin up, spin up. With all the resources that you have. Yeah. Really? You got a lot of customers too. Yeah, which is crazy. Yeah, and think about it because we're using CPU across, obviously, just like running our product services, running the API, running all of our apps. We're using CPU on sandboxing, all the sandboxing that we're doing. And then just like all the things like CI, you know, like you'd be surprised how much more code is getting written. Therefore, there's so many more CI runs. Therefore, you need more CPU to handle that. And so on the whole, and this is not just us, it's everybody that I talk to. I would add training. Training is probably using a sandboxing. So there's a different bucket, right? I'm sure they don't commingle that much. Well, they don't commingle, although like CPU for Anthropic as a whole is like a zero sum game. 

**54:50** You know, like we get our we negotiate our deals with the CSPs and we get our CPU and we have to allocate within Anthropic how much of that goes to all the things I just mentioned, including like research sandboxing versus product sandboxing and otherwise. And so we really are allocating across absolutely everything. and it's just everyone in the industry right now is... Short on everything. Yeah, you used to be able to just like, you know, like scale up on demand into CPU capacity as long as your budget allowed you to, right? And like now it becomes something that you have to capacity plan for in advance, which is, you know, like kind of punching everyone in the face. 

**55:30** It's brand new and people are starting to do it. And so dealing with that means a few things. Like one is obviously just go try to get more CPU. cool, we're doing that as much as we can. But two is we want to be able to, at the infrastructure layer, run it more efficiently, right? Like figure out how we can make sure that we're running our sandboxes as efficiently as we can without compromising on things like tenant isolation or properties that we like, you know, care about for security or otherwise. But then at the application layer, how can we run our applications and our APIs more efficiently? The thing that I'm like most excited about. As of last week, we fully rolled out the Messages API written in Rust, which is one of those like insane feats of like what you're able to do in the agentic coding era that used 

**56:18** to be like we started talking about this like earlier this year. We were like, we're going to hit a CPU wall. There's stuff we need to do. And we've done it. right? Like we've gone multi-region, we've done all these things we need to do. And we were like, we're running this Python monolith and it's like, it's like very CPU inefficient. And so what are we going to do to make the system more efficient? Yeah. So we chose Rust, which was a crazy decision at the time because we had a team of people like on paper, it's obvious, but our team was like, we don't have a single Rust developer in the room. Exactly. Literally. And not only that, But yes, Claude is going to write the code. The humans design the system, right? 

**57:01** So the humans are very involved in making sure at the end of the day, it's a distributed systems architecture, right? And Claude can help you both with actually writing the code, but also helping those engineers bounce around ideas and learn what they need to learn to design that system. But what's most interesting is the two things that mattered most were the plan to do the thing and then the verification of the thing. because the actual implementation was like cloud writing code. And so what we ended up doing was we literally built like an entire fork of the entire API in Rust and then started running shadow traffic through it and verifying that the outputs that we were getting from the Python system and the Rust system were the same. This is like feature flags are all you need. Yeah, literally. Low balancing feature flags. 

**57:45** It's just, I don't know. I never in my career would have had a team say like, yeah, yeah, cool. Do you still have Rust people or no Rust people? Well, they would probably call themselves Rust people now. I mean, there are two big, big steps to Rust. Yeah, exactly. And along the way, we have hired a couple of people who have more experience with Rust. But yeah, but it still is just like you never would have signed up for that. It would have taken years and years and years. Like you wouldn't do it all in one shot. You would have carved out pieces of the system and say like migrate this, migrate that, migrate that. Easily a multi-year effort. But you would never like kind of one in one swoop, right, like port the whole thing over and then just like slowly turn it on. But that is what we ended up doing. And it worked. And so not only do we actually save a lot of CPU, but we our latency has seen a nice improvement, which is pretty cool, too. 

**58:37** And, you know, it's just it's it's a crazy error we live in because you just never could have done that sort of thing previously. Order magnitude of savings on the on the CPU side. Just for people considering this. Yeah, yeah. Okay, order of magnitude, that's really interesting. Ballpark, yeah. Yeah, ballpark. I guess I would say like we've seen somewhere like 40 to 50 percent, something like really crazy. Yeah. And then latency, I think we saw somewhere between like 10 and 20 percent improvements on like time to first token specifically. So a lot of just really cool improvements across. It's higher on like longer running workloads, but so it'll roll out soon. 

**59:16** Yeah. But it'll get faster. Do you fight the urge? You know, I've been involved in some major rewrites. There's always like, what if we add this new thing that didn't exist before in the rewrite? Since we're rewriting anyway, right? Yeah. But then like that add scope. Yeah, yeah. So do you just like avoid that? Is it a pure rewrite? We did. It was a pure. It was maybe the thing that I would call it is like a port. What we did was we did exactly that. We like resisted the urge on all these things. And then, and because of what the team already had to keep up with was a little like swap team that we put together to go and do this was doing this while the whole rest of our team was like building features into the API, right? And they did all the like static analysis stuff that would yell at you because you built a feature over here and you didn't consider Rust over there, right? And like they dealt with all of that, but they resisted the urge to add features themselves or like make the system different. 

**1:00:05** Now their next goal is to go and do some sort of re-architecture type work, including stuff that I think will be more user-facing. Like we're starting to roll out some things that are like, you know, what if we were able to not have you have to like pass back and forth the entire like conversation state, right? And if you didn't have to do that on the Messages API because you were open to, like, you know, a storage of that state for some period of time, right, then, like, especially for long conversations where the context really builds up, like, you get a lot of really great latency and efficiency wins there, too. Like, these are the sorts of things that the team that was doing that port was like, ooh, like, what if we do this, right? 

**1:00:45** Doesn't your caching API have that? So, we have prompt caching, yeah. But you still have to, like, pass back the state of the conversation. Okay. Very, very niche, small subsets. But I guess this is the level. No, this is important. Yeah, yeah, yeah. There's a lot of savings that can be done, but it also turns them into a cloud. Yeah, and then from number of users' point of view, it's small, but if you look at the actual token volume on those types of workloads, it's going to be the majority. I'm very like, what Cloudflare did with durable objects, I feel like every cloud provider should clone. Do you know what I mean? Yeah. Like, right? Isn't that the right shape for a messages API that needs continuation? Yeah, I mean, it is really interesting. because I think what we're, we are experimenting with various, like what is the right shape of like how we should run our compute in order to power all these sorts of things. 

**1:01:31** But it's also interesting because we can't necessarily rely on like, because we get our compute across, we run on all three clouds, we're running all over the place. Like we don't necessarily have like one shape of, here's exactly like the compute, the types of machines that we know we're going to run on and the way that some certain compute works that we can like rely on across the board, if that makes sense, other than like the most vanilla versions of these things. But I think these things are all going to, I don't know, continue to innovate and evolve as time goes on. And so we'll be able to take more advantage of those things. Yeah. I'll just mention if people want open source Cloudflare, durable objects, Rivet is the open source equipment. There are 50 people listening that will kill me if I don't ask this question. You want CPU, they have CPU to sell. Who do I sell to? Who do I email? 

**1:02:16** And what is a good... help them help you, right? Like, what does it look like? Well, I guess, like, email me. Are you sure? I don't know. I'm going to get a lot of emails. But, like, you're looking, like, minimum fleet size, SOC 2, obviously, enterprise. Yeah, yeah. Give me all the privacy guarantees. And for what it's worth, like, so we have a really awesome team that secures our accelerator compute. Yeah. That is now spending a lot more time with us on CPU and, like, where and how we can procure it. to make sure that we can just continue to run our systems at the scale that we need to. And so it's not actually like my job, thankfully, is not to go and run around and try to procure compute. 

**1:03:00** But we do have teams that are doing that, which is really great and really important. But obviously, like, there's a quality bar that matters to us. There's like a scale bar that matters to us. We're not going to, like, go and integrate and run on certain machines if, like, we can't get enough of it. And so all the properties that you might expect of a system at this sort of scale is what matters to us. Then related to the compute problem is the sandboxing problem. People have trouble containing agents in sandboxes. They keep breaking out. What's going on there? What's the state of the art? Yeah. The state of the breakout recently. Yeah. It's interesting. One of the things that we did with Claude Managed Agents specifically is the way that we architected it. We put out this blog post at the time of launching where we talked about we have the brain separate from the hands. And what that means is like the harness runs on a durable server, really ours, right? 

**1:03:49** Oh, wow. Nice pull. Yeah, that's good. And the hands are like an execution environment, like a sandbox or some sort of container, right? Part of the reason why we did this was for latency and reliability and these sorts of things, right? Like if the harness is just running on a durable server and it's only spawning a sandbox or a container to execute when it needs it, that means we can get you tokens back really quickly on your initial request. It means that your sandbox can die, but like your whole agent doesn't die because the harness is durable and over here and the, you know, the agent storing state separately also. And there was a whole product development journey that we went on to get there because our first iteration of an API for agents was just like throw a Claude Code in a sandbox and like shove an API on top. 

**1:04:32** And the reason why we didn't launch that as our like durable, scalable solution is everything that I just said. It was like you've got to boot a sandbox and boot Claude Code before you can even get a token back, right? The sandbox dies and you lose your whole agent. And so reliability wasn't where we wanted it to be. So we architected this way mostly for reliability and latency. But the interesting thing, there's actually the product manager who worked on this from the very beginning was like, the most important property of this architecture is security. And everyone's like, yes, yes, yes. But latency and reliability. And like now. Now they're vindicated. I literally, I apologized to this person yesterday. I was like, I'm now like telling everybody who I meet about what you've been saying for months that nobody was as excited about as this person was. 

**1:05:15** But one of the more important properties of this architecture is you have credentials for any work that the agent needs to execute, right? Like if you're going to go talk to an MCP server or if you're going to go like use a CLI or call some API, whatever you might want to do, right? And like you need to authenticate those things. you need to store those credentials somewhere so that the agent can use them. And the way that we architected this was we have a totally separate vault where those credentials are getting stored. And as once the agent decides, like, I'm going to go and call out to one of these services, the agent only ever sees like a placeholder, right? And like on egress, we have a proxy that goes and like pulls the right credential out and injects it and lets that out. And so what's interesting about that is like the agent is never like in the same place where the credentials are, right? 

**1:06:06** Because in the old world of like just boot the agent in a box and like let it run, you had to send credentials in there. And then you're like doing some sort of like, how do we like scrub this and that and make sure that like the agent can't take that credential and go do something that it shouldn't be doing. It's just like literally a like literal infrastructure wall that says the agents like can't. can't access those credentials and can't do anything bad with them. So that was one thing that was really great. The other nice thing is like on that sandboxing infrastructure, we can make sure that we have really great egress controls. So we have like domain allow lists and things like that, or you just like totally shut off network access so that you can't say, okay, agent wrote untrusted code, that code executes in sandbox, 

**1:06:47** and that code can like reach out to the internet, right? Like now it only can reach out to where you say it can reach out to. So there's a lot of really great properties around all of this within the architecture and within the way that you can configure your sandboxes, make it so that you end up like secure by default. One of the things that's starting to happen is as we're talking to customers who are like, I want to run agents either for, you know, a product for my customers or internally. But I'm like, you know, hearing about incidents where agents are breaking out of sandboxes and bad things are happening. How do I think about these things? I think the two most important things are architecture so that it's like literally impossible for an agent to do something that isn't okay and the configuration you have on those sandboxes. And so one of the really, really nice things about Claude Managed Agents is you just like get that out of the box. 

**1:07:31** And you get something out of the box that like as we learn more and we continue to evolve the properties of those systems, you kind of get that for free. because I think all the words that just came out of my mouth, like brain and hands and credentials, injection and egress and all these sorts of things are just like, if you go back like nine months, like no one would be saying these words, right? Like these are all like relatively new problems that we're solving. And so we want our customers to be able to take advantage of the solutions to those problems as we're even learning about them and solving them ourselves within our infrastructure. Yeah. And internally, we're just upgrading everything to be basically secure by default. Yeah. but it's better. We also unlocked something that we covered in the direct pod which is the CloudBrain Local Hands which is projects. 

**1:08:15** Yeah. Is that the rough shape of what's going on? Yep. That seems right. Yeah. I feel like it's weird because obviously your famous or Claude Code is famous for being the super unsafe dangerously allowed permissions thing on your machine but now you're like finally evolving to where you probably should have started in the first place. Basically I think like all these sandbox leaks right? Are they a fundamental issue with our understanding of sandboxing or container tech, or are we just bad at this? Are we just not doing the allow list? It sounds like we're just not doing the allow list. Yeah, so maybe a distinction that I would make is in a lot of the incidents that people are hearing about and reading about, these are incidents that are happening to models, not just everybody's models, right, 

**1:09:01** that are in training, where the way that you're running these models is you put them in a box and you say, like solve impossible task. I'm going to stress you out. Ready, set, go. Right. And you like can only do what you can do within your environment. And the agent is basically incentivized to try to get out of his box and go solve the problem. When people are running production workloads on our API or on anybody else's API, you're running an environment where like there are safeguards that are running that are like not necessarily running in training where like the whole point is to actually train the model. Right. But in production workloads, first of all, you have those online safeguards is really great. Second of all, you have more of this architecture and infrastructure around what's happening. Like the agent isn't just in a box trying to get out, right? Like the harness is over here, the hands are over there. And it's just like not even a 

**1:09:47** problem that you would have to contemplate. And so I think that some of this is just like people having the right understanding that what's happening, some of these incidents that are happening in model training and otherwise are just things that like wouldn't happen in production workloads where a lot of work has been done to put the right guardrails in place so that those things like never really could happen. The second thing I would say is just like everybody's figuring all of this out like relatively newly because, you know, long-running agents is a relatively nascent thing that everybody is building and trying to deal with. And so everyone's figuring out some of these things on the fly and just racing to put the right, you know, like controls in place. And, you know, some of that is just happening as we go. So I would say it's not, you know, where people are bad at this, right? I think it's just more, it's a nascent field, 

**1:10:33** people are figuring it out. And I think when we talk about things like pacing the frontier, the way that we think about it is really specifically, we're doing all the work that we're talking about right here for all of our agents internally and making sure that all these things are accessible to all of our customers so that like by default, the right guardrails are just in place so that you don't end up having agents doing things that you don't want them to be doing. Fair enough. Because you mentioned pacing, we've got to go there. We also talked about this last time. It is superficially contradictory to advocate for pacing and then also release a new frontier model 11 days later. To me, it just means that it just passed your safety checks, right? Yeah. Everything we talked about earlier on how we launched the models, 

**1:11:19** it's like we are continuing to evolve more and more and more safety properties in place, again, across these, like, I kind of am starting to think about this in these three dimensions. It's like the actual, like, architecture and infrastructure security. Then it's the online safeguards that are running when a model is doing its work. And then it's the offline safeguards classification that's happening on data at rest. So across all those three dimensions, we're putting all the right work in place to make sure that we feel really good that we can put a model out there and it's going to be safe. And really, that's fundamentally what we mean. And so for Opus 5.5, It's an excellent model. It's also really, really great on all the dimensions that we care about across those three things. And therefore, we feel good about releasing that model. 

**1:12:01** So we went for what we thought, what we were hoping is the best possible user experience within this while we continue to hill climb on making those false positives better. But it's obviously like it's still a frustrating experience for people when you're trying to work with a model and then you fall back to a different model. And so it's just something we have to keep working on. I think people also understand it in the safety sense. Like you want more false positives, right? Like, it's a safety issue. We clearly see the whole invasive incident, and you're still giving access. To me, this is like a close cousin of the general model routing problem of do you want a smart router or dumb router? Does that make sense? Yes. Like, because as far as I can tell, the anthropic fallback router or detection thing is a separate model that's online. 

**1:12:46** Once it hits a classifier, there's just logic to what you just need to drop. So it is not the full fable. You're not running full fable at it. And that's going to cause classification issues because it's clearly not as smart as fable. Like, clearly. So then there's the question of, well, then when I have a classification issue, that's like an unjustified fallback. You never need to fall back. I'm just doing my bio homework. But I mentioned bio one time, and then it can fall back. So there's a question. Then this is very similar to the routing problem because your routing models, and how can a dumb model route to a smarter model if it doesn't even recognize this? That's a smart model problem. How do we solve this? It's a hard problem. I think in some super alignment sense, 

**1:13:32** models are good at classifying stuff, but not answering them, right? Not the smartest model can tell this is a dangerous query, even if it can't make any impact on that. I think the super simplifying thing to say would be like, basically you have the smarter model figured out over the one that it needs to detect. I think it's slower at X cost. There's trade-offs. There's trade-offs. I'm not saying it's like the right user experience, but it would probably be a slightly more effective route. I think one thing interesting is you also don't cut off immediately. I've seen cases where it will start answering, start reasoning, and then get hit by a safety filter. So my first example with Fable was throwing the Fable safety card. And it started, I was like, just break down anything interesting in here. 

**1:14:15** starts breaking it down five minutes in. I think it gets into cybersecurity and it's like, oh, we're cutting, we're rolling back to Opus. But it did a lot. There's a lot of context and I see it. It got to a point where it's all about cybersecurity, safety, and then I rolled back. And it's like, it wasn't rolling back my query. It was the combination of reasoning and the whole chain, right? Yeah. Do you have an idea of what's better? Yeah, let's brainstorm. What's better? I think the user experience and how we resolve that user experience is probably, you know, a separate question. Like maybe it's a super smart model to handle it or whatever. But I think that the more appropriate UX that I would imagine for a user is to actually have a more consistent, unified experience with Claude, where instead of getting you falling back and then you can double fall back, 

**1:15:00** there are like logical points in our tree that do do that for, again, like safety reasons and whatever. But I think that UX is just like really convoluted for a user. It's like you almost, you're trying to do something. And I would probably take the case of like a good user. And you're a good user, just trying to sell your biohomework, like assess the cybersecurity, like safety card, totally reasonable. And you get blocked and you fall back. And I think instead it would just be like, you are where our model should have gotten or classifiers or however we want to do this would be smart enough to detect that that is a situation that happens. And just write down the answer for you more concretely while dropping down the nature of the like negative use of that particular capability. So going back to that bio homework scenario, while we may have classifiers that have detected that it is possibly bio. 

**1:15:47** I'll also say like on the API, we went with a spectrum of solutions for this for customers. So first of all, like if you've built something on the API and you run into a block, like you don't have to like continue the conversation on a fallback. That's what we've done in our apps. And we've made that a feature of the API and you can do it like server side within our API. you can choose to like set, I want to do the fallbacks, right? And like, we'll do that. We also have it within our SDKs on the client side. There's helpers for if you want to like manage fallbacks in various ways, or you could choose to like reprompt differently in some way, right? And continue. with the same model. But I do think that there's some in-between interesting solutions that we'll 

**1:16:30** continue to experiment with to figure out how we unblock people and do so safely, but with a good user experience. Yeah. And then you brought up the bit that I was like very similar to routers. I think like, I totally agree with that. I think it has a similar UX problem as well. Like with the router you go through, it's like, let's say you want to do a cost router, right, for the task complexity that you have. Makes sense. Very hard to do. You don't know everything upfront about your task. So you can't like, even with perfect, you just don't actually have perfect information. Even if you could actually understand each model perfectly, which you don't, but even if you did, you also don't have perfect information about your task. And so there's a lot of error associated with your routing logic that you would have to create, whether no matter how good a classifier you produce. And yeah, maybe it's like a super big brain model type of problem, but regardless, it's I think a very difficult one. And in each one of those scenarios, if to find a way to route 

**1:17:17** that properly, it's very, very convoluted. You could expose all that logic to customers like, hey, you know, your task was to center a div, so I'm going to route you to something super cheap. And then there's other ones like detect vulnerabilities, so I'm going to route you to something like really expensive. If you expose all of that, I don't think that's like a great user experience ultimately either. And for that similar class of problem, I think the more idealized UX would be something along the lines of like dynamic effort or dynamic understanding of the level of complexity of your task, which you might actually evolve over time as you learn more and more information about the task. But in that world, if you're kind of like moving between a lot of different models, it actually becomes very, very disjointed. And so like maybe a more unified approach would be something like it's a consistent model, 

**1:18:01** but it's like very, very low effort level or something like that. It's actually very, very cheap and very, very affordable, but it can scale itself up very, very continuously. But you have like a very, very consistent persona towards that model. And obviously speaking about this in the context of like, you know, like our family of models, if you try to even do that across other family of models, other model providers, like the personality, the disjointedness, like starts becoming very, very complicated. I don't know how you solve it in that layer, but at least within like our family of stuff, like I'm more bullish on things that are just more consistent. And those might be ways to solve it more robustly for customers. Yeah, you're not responsible for anyone else, but within Cloud, yes. Yeah, I mean, this is your territory. Yeah. It's nice to see the way you guys think of these problems, right? Intent in and of itself is a hard problem. Like I feel like the models have gotten better, 

**1:18:46** but sometimes people don't do a good job at classifying what they want or they just slider all the way to one extreme or, you know, the model doesn't really understand. You're asking a simple question and it's like, oh, I have a whole HTML landing page now. I just expected a few line output, you know? So there's always a mesh around there. Yeah. I think we, in this conversation where we talked about a lot of safety, we talked about like launching the models, like all this kind of stuff, but it's obviously in pursuit of helping power the workloads for people. And like we discussed the kind of the earlier part of this conversation, we are seeing workloads change quite a bit. I think like in some workloads, intelligence does seem to be increasingly saturated, which is like a new situation. And it's getting to the place where I think there are some class of tasks that are still 

**1:19:32** very, very unsaturated. I actually think there are more than maybe people anticipate, but they're increasingly going into the direction of you get that alpha or that return by doing extremely long-running agentic tasks on things that historically you were not really able to brute force. but maybe you can with a lot more intelligence behind it. And those workloads are really, really fascinating to us, and we see a lot of value in that kind of area, both from businesses getting that value from themselves as well as, you know, like new companies or startups or however you want to frame it who are interested in building kind of like ejected products on top of it. I think if you look at the class of things today, a lot of it tends to be, it's obviously a lot of coding stuff. There's obviously a lot of like enterprise, innovation, acceleration kind of stuff, but they tend to take the shape of automating 

**1:20:15** a very distinct and specific process. And I think in some areas that's actually really starting to work, which is awesome. In some areas it still isn't, so there's still a lot of room to evolve and push the frontier for those models. And then there's this new class of workload, like the bio stuff that we talked about or some of the cyber stuff. It's just a very, very complicated set of hard engineering or hard science problem. And those workloads are increasingly showing up as well for us as a really interesting space to just deliver value to customers. You know, you are at one of the few spots in the world where you're discovering all these workloads and we're automating it. It's like... Yeah, it's pretty crazy. I mean, you know, that's very inspiring. You guys are having so much impact and I'm really grateful for that. 

**1:21:00** We're going to go into a few closing questions, which I thought would be fun to sort of bounce back and forth between the two of you. How do you fight Slop on your teams? So we actually, this has been an interesting debate within our team. We're no different than anybody else, right? I think plenty of teams are talking about this. And what's interesting is what we're trying to figure out is if you're producing a document, you're producing something that you want other people to consume, code, documents, whatever, right? If, like, you should use Claude to help you, of course, right? But if you use Claude to, like, totally generate a thing, you didn't spend any time on like actually reading it yourself and making sure that it was something of high quality then you're putting a whole extra burden on the consumer of that document or of that 

**1:21:45** code or whatever to spend a whole bunch of time trying to parse it and that's just like insane and totally unfair but what's funny is we you know we push back we push on our team like you can't do that right like if you're producing something use collage or whatever but like you have to make sure that it's a high quality thing yeah you as the human are still responsible for the quality right You're still responsible for the quality, but then you get into this really funny thing where because of the clodisms, even just like silly, like, I don't know, there's like an M dash in the middle of the thing, right? Like people's brains trigger on this like, oh, no, I'm reading something that was written by clod and not a human. And I'm like annoyed because I want to be like reading a thing that was like fully produced by not necessarily fully produced by a human, but like that has the quality bar that the human would hold. 

**1:22:30** Right. And so then we get into funny situations where maybe actually like the person did spend a lot of time making sure the thing was high quality, but they use Claude to help them. But there's those little hints of Claude and then the reader is like turned off, you know, like that's like unproductive as well. And so what we have kind of tried to do is like set a bar within our team that's very much like use Claude to help you because it would be really silly if you didn't use this hilariously powerful agent to speed you up in your job and in your life or to just like help you produce better things. But it has to like have a certain bar. And then for the consumer and the reader, if like it really actually is just slop, like send it back, right? Like just say no, just send it back. Like you're not going to waste your time on this. but also don't be like too nitpicky about the little quadisms, right? 

**1:23:17** Because like we have to find the right balance here. And so it's just really funny because we're, you know, we're going back and forth on this stuff within our teams. And just like, so is everybody else in the world right now. We're just not any different. But you have free tokens. Well, sure. But I mean, that doesn't tend to matter quite as much when it comes to life. So you have more than anyone else in the world. You probably see a lot. You guys use a lot. you see a lot more people doing a lot more. Yeah. Yeah, it's totally fair, but it's definitely, it's definitely. Heavily discouraged. Yeah. Engineering slope? Yeah, I mean, same thing, right? But I do think that where we've gotten to is a pretty good spot where like, you know, humans play a large role in making sure like designs of systems are good, right? 

**1:24:01** And like implementation tends to be clawed, right? And like, we're in a pretty good spot where for like a lot of the code that gets written, And like, all you really need is just like a quick stamp and you can keep going. And like, you don't necessarily have someone like deeply reviewing every single line of the code because you care more about like the overall plan, the overall like design is in good shape, less so than like the exact perfect implementation. And obviously rely on a whole bunch of verification to make sure the thing that you're going to ship is high quality. And so it's actually been like less of a conversation these days on like code quality being slop in part because collage has like gotten better, but in part because we have have a lot of the right verification in place to make sure that we're holding a high bar. I see. Trust your tests. Yeah. 

**1:24:45** Any product PMing slop, you can ship features a lot. You can put stuff out in a day. So he said this to me recently. Do you remember what he said to me? Tarek tweeted this. The right way to use model capabilities is not to ship 10x more features into prod. Spend more time understanding your users, trying experiments, building, whatnot. But basically, you could very easily just keep shipping random stuff, but you should actually think about it. You should spend more time kicking off that first prompt. I know now today he's like, kick off plan mode. We don't need it anymore. But outside of plan mode, you should think about what you're doing, right? It's very easy to put out a lot and you can get products solved. Yeah. I mean, I tend to kind of go on this rant about, there's a lot of just like fundamentals 

**1:25:30** about how software gets like, and products get planned and shipped and otherwise that like, doesn't need to change just because agents are like helping speed up certain parts of the process. But there's this inherent like almost laziness is starting to come out from a lot of people where it's like, oh, I don't have to like think about some part of the process at all because the agents are doing it and helping me or whatever. But like those are still like incredibly foundational parts of the process that like should be things that you're spending your time and energy on. And I think that's kind of the spirit of what he's getting at here. It's just like as a founder or creator, let's call it as well. It just feels like because the cost of experimentation or exploration is so low now that I should be doing more of that. And he's basically saying to not do 

**1:26:16** that, to like use it in service of a very intentional goal that I start from the start, which like obviously I'm coming around to. It's just hard. It's like you have this toy. does everything. It's just like, throw it at the wall. Yeah, there is scope too. So like the blog post of today was two weeks to make Cloud AI faster. There were 12 things. We hit, there were 13 things. We hit 12 of the 13 targets in three days, right? Well, you could just stay, okay, it's a two-week sprint. We'll do the 13. But instead, there's 20 things you push bound. This is not product slop. It's not new products. It's refactoring. But still, you guys do a very good job at outputting a lot but keeping high bar quality. Yeah, nice. Okay, part two or three. This is going to make Angela very uncomfortable. 

**1:27:00** This is Angela Tweet Corner. I will tell... Nice. I don't know. I will tell people nonstop to... And I'm pretty sure you've deleted stuff. Because I'm like, there's stuff in your deletions that I'm like... That you liked? Yeah, I know. It really altered my DNA. Oh, I appreciate that. What do you think about the most? what do you refer people to the most that you want to sort of bring up? And I can pull it out for you if you want. I actually, I don't think I have a favorite, honestly. Oh, come on. No, I really do. Okay, I guess maybe I like the one that you say where it's half doing something is an expensive way of not doing it. Maybe that's my favorite. That's very on topic for us today. Okay, nice. Okay, thank you. Love it. It's it, yeah. 

**1:27:45** I guess, you know, obviously you're in a different job now and you don't do this anymore. but like uh i think this is a power tweet this is like a naval robincon there's naval as you yeah that do this uh-huh and like what mode what mode of what like what framework of mind frame of mind are you in that's a good question yeah no this was a very long time ago um i went through a period of my life where i was like reading constantly actually i found myself like very bored um of the stuff that i was doing and i felt like i was just like not being like intellectually challenged. And so I actually went on this a bit of a bender where I think I read something like 200 or so books in like a year. And I was just curious about like, I just wanted 

**1:28:32** to like read everything. And I was missing a little bit of like the kind of like intellectual, like, I don't know, like muscle building you get when you're like in school, where they like, this is a new topic, you got to learn. It's like tough. It's like not super fun, actually, but you are really like stretching yourself. And I felt like after just like working for a couple of years, I just like stopped doing that. Like, yeah, the job's like interesting. And yes, it's like challenging, but it doesn't have like that like intellectual bit to it. So anyway, I went on this bit of a bender, a reading bender and I like read like 200 bucks in a year. It was great. I had like a really long commute. So it was like, it was a good use of my time. Very long like Caltrain ride. But I ended up coming out of that and being like, I was just like thinking a lot as a result of that. and at the time it really wasn't to build like a Twitter follower or anything. 

**1:29:17** I was just like tweeting into the like abyss. There was like, I have like no followers. These were like tweets for myself and I was like practicing a little bit of like, how would I try to compress the idea that I have in as few words as possible? It was kind of like the personal test I had for myself. And then I guess people just started liking it. And so I just kind of just kept going at it. And then I got too busy with work and I stopped doing it. I gotta ask after all the reading, any book recommendations, blogs, long articles? My favorite one still is probably The Black Swan by Nassim Taleb. That was like one of those, to me at least, it was like a bit of a life-changing philosophical book where I was like, that's a completely different way to look at the world. 

**1:30:01** You know, I was thinking of Black Swan when the fable thing happened, right? It's a thing that you didn't know could happen. Now that it happens, it permanently changed your framework of how things work. Exactly. And the idea is so simple, but I think he did a great job of just really hitting it home and articulating it and how it's a common property of so much of the world. So I really love that book, and that's probably my favorite. And for listeners, if you're bored with that one, you do Fool by Randomness, also by Taleb. Did you like Hinky, Fragile? Less so. The more famous he got, the more intolerable he became. but also question for kaylin my if you do have one do you have a similar like this is a book i recommend to everybody no that's a good question i need to think about it it is funny because i do 

**1:30:45** think we have very different styles on this front like she's no longer like in fortune cookie tweeting mode but i do think that like you stop that but i do think a big part of how you work and how you work with other people is like philosopher mode sometimes you know fortune cookies and slack oh i i do actually not intentionally i think it might just be somewhere i think that's his core success of succeeding in topic is like you have to be very good on slack yeah oh you have to be very good right um it is it's a pretty intense place but i think i'm a little so i'm kind of psycho about like inbox zero on both email and slack it's easier on email these days than slack um but i like pretty aggressively prune like here are the channels that i'm like actually in and paying attention to you versus the ones that are like muted below the line sort 

**1:31:30** of thing. And I'm like always making sure that I'm on top of the ones that I need to be on top of. But it is it's basically it feels like a full time job. Like there are days where I'm like, I could cancel all my meetings and write no documents, do no work. Right. And literally just sit there like on Slack the entire day. And I would have done what felt like an entire day's worth of work just by like engaging on Slack with all the things. It's pretty wild. No, but that's horrible. So that's exactly how I feel. I feel like you do all this. You put in all this effort. And yes, like you met the mark on like responding to people and getting certain things over the line. We didn't actually produce the output that you wanted to. And like, that's like a, that's like personally a very hard thing for me to deal with. So I try to be like, I need to like dedicate time to like actually thinking and actually like trying to produce something in order to feel like I'm like actually. And ignore and drop stuff, right? 

**1:32:15** You're going to drop stuff. Yes, I have gotten a lot of feedback. I'm a little hard to reach on Slack. Yeah, pretty terrible thing that happens now is I get a lot of Slacks saying, can you get Angela to respond to this? And I'm like really trying to write a doc here. So you guys don't have like a personal cloud assistant something? I don't know if it's going to help you. So why has no one written in a personal EA for each of you? Yeah, well, so we do have a human one also. We have plenty of cloud help in Slack for sure. But I do think actually the types of Slacks that I'm like when I'm pruning through and getting are often just like our team is like blocked on something because there's some hard decision that needs to happen or whatever it is, right? 

**1:33:00** And it's like, it is just like a form of, whether it's in Slack or some other way, right? Like it's a form of getting the team unblocked, like executing in high quality. That is my job anyway. And it like happens to happen a lot in Slack. But I do agree that it ends up feeling like, what did I even produce when you get through a whole day worth of literally just like going back and forth with people and trying to solve problems. You produced a functioning team. Yeah, exactly. That's unblocked. So I will give you a props for that as a former manager. That is, yeah, yeah, yeah. You don't feel like you produce anything, but like you produce the team. Yeah. Yeah. Speaking of your team, I was going to go to you next, Katelyn. DevRel Corner. Oh, nice. We are, I know you're really hiring DevRel, so I'm going to make a call offer for developer relations. Nice. You hired Addy Osmani recently. 

**1:33:45** Yeah. Great get. Lance, you know, you still don't have a team. what do you guys want to hire for? You know, this is it to talk about it. And I'm happy to trade notes. Yeah, well, props to you because I will say when- I'm the all-source Anthropic recruiting, Katelyn recruiting team. Literally, literally. It's fun. And it's actually not even just recruiting. Like I'm gonna toot your horn a little bit. So when I joined Anthropic, we, you know, we're a small team. We're like all things considered, we're an extremely small team. And so we run the platforms, the API business and all of our product infrastructure. And we had, like, a very nascent DevRel function within Anthropic that was, you know, like, not necessarily within the product and engineering org. 

**1:34:30** And when I joined, we were like, okay, we, like, need to grow up a little bit and need to figure out how to, like, engage with the community, engage with the world. And we need to, like, really invest in this DevRel function. And we ended up spinning up a DevRel team within Platform and a DevRel team within CloudCode, in part because it's like two pretty different like developer ecosystems that we're trying to engage with across the API business and Agenda coding, right? And so we ended up doing it as like two separate teams, but kind of like dotted line to each other and spend a ton of time together. but it was something that I was going to be responsible for like DevRel which I've spent plenty of time with DevRel before Stripe or otherwise but it was never something that I was responsible for and I was like I have no idea what I'm doing and you were like I got you 

**1:35:16** like here are the three pillars of DevRel like here's how you think about it which is really great across like product content community and now I can like rattle that off to people and they're like wow you really understand DevRel I'm like no so it's Tommy you're like I copied that that was not a ritual That post is apparently like the default reading for Google when they onboard new DevRel. Yes. It's really funny. Yes. It really like this really helped me because I had no idea what I was doing. But then on top of that, I don't have a manager for the DevRel team within Platform right now. And so that's one of the things that I'm hoping to hire. And our DevRel ICs report to me directly, in part because I think it's a really, really, really hard problem right now. but also in part because I find it really fun and I'm having a lot of fun working with the team. 

**1:36:03** But really specifically, like what's hard on platform is platform in some sometimes in our like public narrative, like our platform business is a really large business, but it can kind of get lost in between research and policy and all this work that we're obviously doing. That's like our most frontier important work. And then our applications like Claude Code and Cowork and all the things happening over here. When you think... about Anthropic and you think about our public narrative, those are some of the things that often come to mind and less so that we're running this like massive developer platform business that's really important and so many people are building on top of. And so a big goal that we have with our DevRel team within platform is figuring out not only like the really classic, like how do we better engage with like, you know, product, like 

**1:36:46** what people are building and how we get feedback back into the product and these sorts of things, but a lot of it is just like finding our voice and finding what our brand needs to be and what our narrative needs to be, and like who we even are going to be in the community and not kind of get lost in all of the things that are happening. And so that I think has been interesting because I think the brand that we've started to spin up for our DevRel team, in part because actually Lance joining our team was you connecting me with Lance, but Lance is highly technical in the way that he goes about DevRel is very much like teaching, right? Like he writes a lot about memory and managing costs and these sorts of things. And he goes really deep. He builds evals. He has a lot of really technical content that he puts together. And I really like that idea, that idea of our DevRel team being kind of like thought leaders for people within what is a really nascent, really hard, really evolving space. 

**1:37:36** Educators, thought leaders, explainers of things. There's another version of this I really like called the Master Builder, which is I will build the things with the platform to show you what's possible. And then you'll be like, oh, I didn't know that was possible with the Claude Platform. And then you just go. And this is a thing stolen from Lego. because Lego, how do they sell Legos? They have master builders. They build things with Legos and you're like, I want to do that. I want to be like this person. And then they do that. Nice. That's awesome. Yeah. And CJ, the other person on our team, who's really great as well, I think he also does a lot more of that sort of like, you know, writing cookbooks and like putting out examples and really engaging with the community on X. 

**1:38:16** Cookbooks is funny, right? Like when APIs launched in 2023, let's call it for LMs, very important. Now, I don't know. Yeah, well, it's interesting because you're less in the spot where you'd like copy paste the code and like use it, right? But I do think it's like it shows the art of the possible for people. And just like what even is the use case, right? And like how can you go about accomplishing it? But I hear you that like, you know, the code itself is maybe less. Like we for the longest time had this like there's an engineer on our team who wrote our cookbook on computer use. and it like haunts him to this day because it was like massively complex and like, you know, kind of a beast of a thing that I think, you know, a lot of people got use out of at a time 

**1:39:00** when it's like, I'm going to build a computer use implementation and I'm going to deal with all the screenshotting and all the things, right, that are happening between the messages API. Yeah, we're there for the hackathon that you guys launched in the office. Yeah, yeah. And like that was the era when you like needed to have that sort of thing. And now, you know, it's much more about We have a skill like the Cloud API skill. It's like really, really excellent at helping you either within Claude Code or any other coding agents to build a really high quality integration. And now like these sorts of things like I'm going to build a computer use integration. Like how do I go about doing that? Like very few people I think slash hope are reaching for the big gnarly cookbook and more so they're loading up the skill and encoding agent and working. Yeah, or they're just putting it to a reference architecture. Yeah, it actually does much better. like increasing or seeing that for a lot of the things 

**1:39:45** that you might want to have different implementations on, typically at the harness layer, but there could be other variants. You just point Cloud at it and you say, this is the reference architecture I want. Yeah, then you build it. Fun fact, the Cloud API skill that helps SQL build against the API is owned by our DevRel team. So that was built by Lance. As they should. Right, exactly. This is the most high leverage form of DevRel is to distill everything that is the best practices that's going to go into those agents. Yeah. One of the reasons, It's weird because I'm regarded as this DevRel person or authority. But there's some part of me that just wants to tell you you don't need DevRel. You get so good you don't need it. Right. Or you turn the engineers, the leaders, into the public faces. 

**1:40:30** Because all you really are trying to do is public faces and here's how we do things here. And that's kind of it. You're anthropic. You don't need people to hear about you. So I think to me, when Boris burst onto the scene, it seemed like a very like, hey, I'm Boris and the creator of Claude Code, blah, blah, blah. That's how we do things. Yes, he has the day job, but he's also doing that functionally. So why doesn't everyone just do that? Yeah, I mean, it's a really good question. And there are teams that have done this really well. I think the Cloudflare team is a really great example. They're all pumping out stuff. Non-stop. Yeah. And to me, that's actually a very visible sign of a good manager is that they're all happy at their jobs. They're all like publicly identifying. They're not quitting. Yeah, totally. It's interesting because I think 

**1:41:17** this is actually something we are trying to do more of with the platform team and with our team and folks who, you know, can kind of just get out there and like be part of the community and be speaking and be on X and be wherever they are, right? I think the problem for us is just like having time and kind of knowing the right way to do that stuff at high quality. So I see the DevRel team in some ways as just being like the experts in doing this sort of stuff at quality. They can do it all day, every day, and they can help enable the rest of the team to think about how to do this with the small amount of time that they are able to spend on this sort of stuff to do it at high quality, too. Yeah. So I call this kind of the unbundling of DevRel into a front office and a back office. So like a typical person who does this job does the whole thing. 

**1:42:02** Start to finish, they're kind of autonomous. But actually you can sort of build up this back office that lets your product leads and engineering leads actually take the spotlight with a simple contract of like, okay, you sit down with me for 30 to 60 minutes and we'll do the brain dump and then you're done. You go back to work. And then we'll like ghostwrite for you, whatever. But like that team of like production people, writing people, whatever is sort of provides the service to the rest of the people. and I think that to me that's like kind of the future of this stuff to be honest because people like don't actually really want to hear from DevRel they want to hear from Lance and like you know and you have like lots of Tariq you know and Lydia you got Lydia through Bun 

**1:42:45** you know exactly but like he he does the job he does the job and so like you want more of those because people people want to hear from people in the product not like who are like paid shows I paid spokesperson. Which I get a lot of. And that's fine. That's a job. Somebody has to do it. Reasonably, you can expect people who are working on engineering to not be good at it. So you should pay someone who specializes in it. Specialty is a skill. I think there is a back office, front office separation that can emerge. Yeah, that is really cool. Yeah. And my analogy would be like everyone hires, but you have an HR department. HR department enables you to hire. Indeed. You hire. Yep. Yeah. Makes a ton of sense. Cool. That was my soapbox. 

**1:43:31** Closing questions. You've been very generous over the time. Any parting thoughts or calls to action? What's something cool on Opus 5.5? What's something people should try to push the bounds with you, Opus 5.5? You guys have been using it for the longest. Yeah. I mean, it's really cost efficient. It's less verbose. All the things we mentioned, the vibes are really, really high. It's been fun. Yesterday was a really fun day for us because finally again, And I got like a whole slew of texts from friends and people who were just like, amazing model. Thank you. Like one of my old bosses messaged me and he was like, Opus 5-5 just saved our butts in an incident. Right. And it's like, OK, great. Like we're back is really the energy that we feel. And so I think things that like maybe you wanted to experiment with something and like cost was something that was front of mind for you. 

**1:44:18** Right. and token efficiency and these sorts of things, that kind of might have held you back from really firing it off and letting a model run. I think those are the sorts of problems you should return to now with Opus 5.5 because I think you'll be able to get a lot further than you maybe otherwise would have been able to with models that were less cost-effective. I think you should try something with a multi-agent setup. I think you will find it to be very good. Any other hints, by the way? I mean, multi-agent is very broad. Nope. I think we'd be very curious to hear what people build. But yes, it should be extra good. Yeah, including, let's say, roles that are not strictly defined, but they are assigned among themselves. Right? Which is, to me, the new thing. 

**1:45:00** You can assign roles. You can always assign roles. You can give tokens jobs. But this is like, they're giving themselves jobs. Yes. I guess, say more on multi-agent, right? Like, should we use sub-agents? What do you... How should people think about multi-agents? This is pretty good in a sense of like basically, generally, you want to have one lead and you want to like split up like a bunch of different stuff. And as long as you can have a task that you think could be generally paralyzable and they share uniform context across it, I think you will get a lot of gains. Now, of course, it depends on the problem that you're pushing at. But like increasingly, this is a very superior way for us to handle a lot of these things. And especially if every sub-agent is, or whatever you want to call these sub-leads, they are asynchronous and they're not blocking of each other. 

**1:45:47** That is, like, the only two bits I would say you should care about in your architecture. Other than that, like, let it rip and, like, see what Claude designs for you. And increasingly, I think a lot of this architecture stuff will fall into this direction. Like, Claude will get smarter and smarter. It's going to learn how to figure ways to, like, split itself, clone itself, adjust itself to the architectures that make sense. You just need to prevent it. You just need to make sure it can, like, parallel process. So it needs a shared state and it needs to like not block its sub agents. And as long as you contain those two principles, honestly, I think people should experiment and probably try some really crazy stuff. And I wouldn't be surprised to see some like really interesting things on top of that, that maybe they felt like there was some intractable problem that was really annoying and they'd keep pushing for. like this could actually probably just like crush it and maybe one shot or something. Excellent. 

**1:46:30** All right, everyone go build. Cool. Cool.