# Katelyn Lesse – Evolving Claude APIs for Agents, Anthropic AI Engineer · 2025-12-04 Katelyn Lesse, who leads the Claude Developer Platform team at Anthropic, explains how the platform is evolving to help developers build powerful agentic systems using Claude. She details three key areas: harnessing Claude's capabilities through features like extended thinking and tool use, managing context with MCP, a memory tool, and context editing (which combined to yield a 39% performance bump), and giving Claude a computer via the code execution tool and agent skills. The talk also covers challenges like container orchestration for Claude Code on web and mobile, and the importance of letting Claude work autonomously in a sandbox environment. ## Questions this episode answers ### How does Anthropic's memory tool and context editing improve Claude's agent performance? Katelyn Lesse explains that Anthropic introduced a memory tool (a client-side file system) to store context outside the window and retrieve it when needed, along with context editing to clear old tool results. Combining these gave a 39% performance boost on internal evals, underscoring the importance of keeping only relevant context in the window for better outcomes. [5:13](https://aie.addtry.com/e993f8af-e5a3-4166-ae91-9110820cabb0?t=313000) ### What are agent skills in the Claude API and how do they provide domain expertise? Katelyn Lesse describes agent skills as folders of scripts, instructions, and resources that Claude can use with the code execution tool. Claude decides when to pull a skill into context based on the task and its description, providing domain expertise—for example, a web design skill ensures landing pages follow a specific design system. [10:01](https://aie.addtry.com/e993f8af-e5a3-4166-ae91-9110820cabb0?t=601000) ### How does Anthropic's code execution tool allow Claude to work autonomously? Katelyn Lesse states that Anthropic released a code execution tool enabling Claude to write and run code in a secure sandbox environment. The platform handles container orchestration and security, so developers don't need to manage infrastructure, allowing Claude to autonomously perform tasks like making an animation more sparkly by running the code. [9:15](https://aie.addtry.com/e993f8af-e5a3-4166-ae91-9110820cabb0?t=555000) ## Key moments - **[0:00] Intro** - **[1:58] Capabilities** - [2:08] Claude's thinking time scales performance, and its API exposes a token budget to control reasoning depth. - [3:10] Claude's API supports built-in and custom tools for reliable tool calling in agents. - **[3:50] Context** - [4:32] Anthropic's Model Context Protocol (MCP) standardizes how agents interact with external systems like GitHub and Sentry. - [6:47] Combining memory tool and context editing yielded a 39% performance increase in Anthropic's internal benchmarks. - **[7:38] Computer** - [7:51] 'If Claude has access to writing code, and if Claude has access to running that same code, it can accomplish anything.' - [8:22] Launching Claude Code on web and mobile required solving container orchestration, security, and session persistence at scale. - [9:51] Anthropic predicts the future of agents is models working autonomously in sandbox environments. - **[11:23] Summary** - **[12:45] Outro** ## Speakers - **Katelyn Lesse** (guest) ## Topics Model Context Protocol (MCP), Agent Skill Design ## Mentioned Anthropic (company), Claude (product), Claude Code (product), MCP (product) ## Transcript ### Intro **Katelyn Lesse** [0:21] Good morning. Um, so first, let's give a huge thank you to Swix and the whole AI engineer organizing team for bringing us together. I'm Katelyn, and I lead the Claude Developer Platform team at Anthropic. Um, so let's start with a show of hands. Who here is integrated against an LLM API to build agents? Okay, I'm talking to theright people. Love it. Um, so today I want to share how we're evolving our platform to help you build really powerful agentic systems using Claude. So we love working with developers who do what we call raising the ceiling of intelligence. They're always trying to be on the frontier, they're always trying to get the best out of our models and build the most high-performing systems. Um, and so I want to walk you through how we're building a platform that helps you get the best out of Claude. Um, and I'm going to do that using a product that you hopefully have all heard of before. Um, it's an agentic coding product. We love it a lot, and it's called Claude Code. So when we think about maximizing performance, um, from our models, we think about building a platform that helps you do three things. Um, so first, the platform helps you harness Claude's capabilities. We're training Claude to get good at a lot of stuff, and we need to give you the tools in our API to use the things that Claude is actually getting good at. Next, we help you manage Claude's context window. Keeping theright context in the window at any given time is really, really critical to getting the best outcomes from Claude. And third, we're really excited about this lately. We think you should just give Claude a computer and let it do its thing. So I'll talk about how we're, we're evolving the platform to give you the infrastructure and otherwise that you need to actually let Claude do that. ### Capabilities **Katelyn Lesse** [2:08] So starting with harnessing Claude's capabilities. Um, so we're getting Claude really good at a bunch of stuff, and here are the ways that we expose that to you, um, in our API as ideally customizable features. So here's a first example. Um, relatively basic, Claude got good at thinking. Um, and Claude's performance on various tasks, um, scales with the amount of time you give it to reason through those problems. Um, and so, uh, we expose this to you as an API feature that you can decide, do you want Claude to think longer for something more complex, or do you want Claude to just give you a quick answer? Um, we also expose this with a budget. Um, so you can tell Claude how many tokens to essentially spend on thinking. Um, and so for Claude Code, um, pretty good example. Obviously, you're often debugging pretty complex systems with Claude Code, or sometimes you just want a quick, um, answer to the thing you're trying to do. And so, um, Claude Code takes advantage of this feature in our API to decide whether or not to have Claude think longer. Another basic example is tool use. Claude has gotten really good at reliably calling tools. Um, so we expose this in our API with both our own built-in tools like our web search tool, um, as well as the ability to create your own custom tools. You just define a name, a description, and an input schema. Um, and Claude is pretty good at reliably knowing when to actually go, um, and call those tools and pass theright arguments. So this is relevant for Claude Code. Claude Code has many, many, many tools, and it's calling them all the time to do things like read files, search for files, write to files, um, and do stuff like rerun tests and otherwise. ### Context **Katelyn Lesse** [3:50] So the next way we're evolving the platform to help you ma-maximize intelligence from Claude, um, is helping you manage Claude's context window. Getting theright context at theright time in the window is one of the most important things that you can do to maximize performance. But context management is really complex to getright, um, especially for a coding agent like Claude Code. You've got your technical designs, you've got your entire code base, um, you've got instructions, you've got tool calls. All these things might be in the window at any given time, and so how do you make sure theright set of those things are in the window? Um, so getting that contextright and keeping it optimized over time is something that we've thought a lot about. So let's start with MCP, Model Context Protocol. We introduced this a year ago, and it's been really cool to see the community swarm around adopting, um, MCP as a standardized way for agents to interact with external systems. Um, and so for Claude Code, you might imagine GitHub or Sentry. There are plenty of places kind of outside of the agent's context where there might be additional information or tools or otherwise that you want your agent to be able to interact with or the Claude Code agent to be able to interact with. Um, and so this will obviously get you much better performance than an agent that only sees the things that are in its window as a result of your prompting. Uh, so the next thing is memory. So if you can use tools like MCP to get context into your window, we introduced a memory tool to help you actually keep context outside of the window that Claude knows how to pull back into the window only when it actually needs it. Um, and so we introduced the first iteration of our memory tool as essentially a client-side file system. So you control your data, but Claude is good at knowing, oh, this is like a good thing that I should store away for later, and then, uh, it knows when to pull that context back in. So for Claude Code, you could imagine, um, your patterns for your code base or maybe your preferences for your Git workflows. These are all things that Claude can store away in memory and pull back in only when they're actually relevant. And so the third thing is context editing. If memory helps you keep stuff outside the window and pull it back in when it makes sense, context editing helps you clear stuff out that's not relevantright now and shouldn't be in the window. Um, so our first iteration of our context editing is just clearing out old tool results. Um, and we did this because tool results can actually just be really large and take up a lot of space in the window, and we found that tool results from past calls are not necessarily super relevant to help Claude get good responses later on in a session. And so you can think about for Claude Code, Claude Code is calling hundreds of tools. Um, those files that it read, otherwise, all these things are taking up space within the window. Um, so they take advantage of, um, context management to clear those things out of the window. And so, um, we found that if we combined our memory tool with context editing, we saw a 39% bump in performance over, uh, over the benchmark on our own internal evals, um, which was really, really huge. And so it just kind of shows you the importance of keeping things in the window that are only relevant at any given time. And we're expanding on this by giving you larger context windows. So for some of our models, you can have a million-token context window. Combining that larger window with the tools to actually edit what's in your window maximizes your performance. Um, and over time, we're teaching Claude to get better and better at actually understanding what's in its context window. So maybe it has a lot of room to run, maybe it's almost out of space, um, and Claude will respond accordingly depending on how much time, uh, or how much room it has left in the window. So here's the third thing. Um, we think you should give Claude a computer and just let it do its thing. We're really excited about this one, um, because there's a lot of discourseright now around agent harnesses. Um, you know, how much scaffolding should you have? ### Computer **Katelyn Lesse** [7:51] How opinionated should it be? Should it be heavy? Should it be light? Um, and I think at the end of the day, Claude has access to writing code, and if Claude has access to running that same code, it can accomplish anything. You can get really great professional outputs for the things that you're doing just by giving Claude runway to go and do that. But the challenge for letting you do that is actually the infrastructure as well as stuff like expertise, like how do you give Claude access to things that, um, when it's using a computer, it will get you better results. So a fun story is we recently launched Claude Code on web and mobile. Um, and this was a fun project for our team because we had a lot of problems to solve. When you're running Claude Code locally, Claude Code is essentially using your machine as its computer. But if you're starting a session on the web or on mobile and then you're walking away, what's happening? Like, where is that where is, um, Claude Code running? Where is it doing its work? Um, and so we had some hard problems to solve. We needed a secure environment for Claude to be able to write and run code that's not necessarily, like, approved code by you. Um, we needed to solve or container orchestration at scale. Um, and we needed session persistence, um, because, uh, we launched this and many of you were excited about it and started many, many sessions and walked away, and we had to make sure that, um, all of these things were ready to go when you came back and, um, wanted to see the results of what Claude did. So one key primitive in this is our code execution tool. Um, so we released our code execution tool in the API, um, which allows Claude to run write code and run that code in a secure sandbox environment. Um, so our platform handles containers, it handles security, and you don't have to think about these things because they're running on our servers. Um, so you can imagine deciding that, um, you're you want Claude to write some code and you want Claude to go and be able to run that code. And for Claude Code, there's plenty of examples here, um, like make an animation more sparkly that, uh, you want Claude to actually be able to run that code. Um, so we really think the future of agents is letting the model work pretty autonomously within a sandbox environment, and we're giving you the infrastructure to be able to do that. And this gets really powerful once you think about giving the model actual domain expertise in the things that you're trying to do. So we recently released agent skills, which you can use in combination with our code execution tool. Skills are basically just folders of scripts, instructions, and resources that Claude has access to and can decide to run within its sandbox environment. Um, it decides to do that based on the request that you gave it as well as the description of a skill. Um, and Claude is really good at knowing, like, this is theright time to pull this skill into context and go ahead and use it. And you can combine skills with tools like MCP. So MCP gives you access to tools and access to context, um, and then skills give you the expertise to actually make use of those tools and make use of that context. Um, and so for Claude Code, a good example is web design. Maybe whenever you launch a new product or a new feature, um, you build landing pages. And when you build those landing pages, you want them to follow your design system, and you want them to follow the patterns that you've set out. Um, and so Claude will know, okay, I'm being told to build a landing page. This is a good time to pull in the web design skill, um, and use theright patterns and, and design system for that landing page. Uh, tomorrow, Barry and Mahesh from our team are giving a talk on skills. They'll go much deeper, and I definitely recommend checking that out. ### Summary **Katelyn Lesse** [11:23] So these are the ways that we're evolving our platform, um, to help you take advantage of everything that Claude can do to get the absolute best performance for the things that you're building. First, harnessing Claude's capabilities. So as our research team trains Claude, we give you the API features to take advantage of those things. Next, managing Claude's context. It's really, really important to keep your context window clean with theright context at theright time. And third, giving Claude a computer and just letting it do its thing. So we're going to keep evolving our platform, um, as Claude gets better and has more capabilities and gets better at the capabilities it already has. We'll continue to evolve the API around that so that you can stay on the frontier and take advantage of the best that Claude has to offer. Um, second, as, uh, memory and context evolve, we're going to up the ante on the tools that we give you in order to let Claude decide what to pull in, what to store away for later, and what to clean out of the context window. And third, we're really going to keep leaning into agent infrastructure. Some of the biggest problems with the idea of just let Claude have a computer and do its thing are those problems that I talked about around orchestration, secure environments, and sandboxing. And so we're going to keep working, um, to make sure that those are, um, ready for you to take advantage of. Um, and I'm hiring. We're hiring at Anthropic. We're really growing our team. Um, and so if you're someone who loves, um, building delightful developer products, um, and if you're excited about what we're doing with Claude, we would love to work with you across engine, product, design, um, DevRel, lots of functions. ### Outro **Katelyn Lesse** [13:03] So please reach out to us. And thank you. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.