Anthropic 内部：AI 如何重塑软件工程实践

Much-improved AI tooling is changing how we build software, and I want to take a peek into how the future of software engineering may unfold under its influence. What better place for that than with tech’s most “AI-pilled” teams: the AI labs themselves.

So, I visited the two leading AI labs to see how teams and engineers do things day to day. In this article and in an upcoming follow-up, I’ll share what I learned about how AI is reshaping software engineering principles many of us are accustomed to – and what’s stayed mostly the same despite the AI wave.

In a later article, we’ll compare findings from Anthropic and OpenAI to see what their ways of working might mean for the overall direction of software engineering.

Inside Anthropic’s HQ (left). AI development milestones framed on the wall (right)

Thanks to Anthropic for showing me inside their lab in San Francisco. I talked with four people:

• Katelyn Lesse, Head of Engineering for Claude Platform, whose organization owns the infrastructure that Claude runs on
• Jarred Sumner, creator of Bun, now at Anthropic on Bun and Claude Code
• Thariq Shihipar, who works across Claude Code engineering and education
• David Hershey, at Anthropic’s Applied AI organization in a role resembling a sales engineer, working with customers like Cursor, Cognition, and Perplexity

Thanks to them, I got a sense of where things are headed at the leading AI lab – and possibly for the wider industry.

Before we continue, The Pragmatic Engineer will be on summer break for the next week and a half. This means no Thursday article this week, and no articles next week. I appreciate your understanding and support!

Back to today’s deepdive, we cover:

• Complex & long: Claude Managed Agents. One of the most complicated projects took the Claude Platform team six months to ship, and created a new primitive to use at the agent infra level. Infra projects still need re-architecting mid-way through and take time to get right.
• Twelve-month project done in 11 days: Bun rewrite to Rust. Migrating a 500K+ line project to another language used to take a small team a year, making it impractical. With Fable and $165K of tokens, it recently took the creator of the project less than two weeks.
• Changing engineering practices. Inside the AI lab with more than 3,500 employees, prototyping is more fluid, verification is more time-consuming than implementation, code review and testing are increasingly done by AI.
• Team-level changes. Design is more ongoing and less upfront, teams work on more projects, a maximum of two engineers per project, and more.
• Still the same: two-pizza teams, planning is important, PRDs are relevant in complex projects, context switching is a challenge, the ratio of time spent on coding vs testing not changing that much.
• Changing the “standout” software engineer archetype? Deep understanding, including of a layer below what you work on, is valuable, along with the ability to coordinate work.
• Will AI replace software engineering? The more hands-on software engineers get with AI at the lab, the less they fear their jobs are going away.

1. Complex & long: Claude Managed Agents

The Claude Platform team’s most complex project in the past year was building Claude Managed Agents, a pre-built harness for production agents that runs in the cloud on infrastructure managed by Anthropic, or on your team’s own infrastructure, with any sandbox you choose. The project took around six months from idea until launch in April. Katelyn Lesse, head of engineering for Claude Platform, shared the story.

With Katelyn Lesse, at Anthropic

Claude Platform

This team sits between the model/accelerator layer (Claude models operate on GPUs) and the product/application layer (with products like Claude Code and Claude Cowork):

Where Claude Platform sits inside of Anthropic

Katelyn on what the Platform team does:

“We’re on the ‘token hot path.’ The prompt comes in, then we tokenize it. Then, things like safeguards and billing all happen within our layer.”

What the Claude team calls “Platform,” I think more of as “API.” Claude Platform operates the API, and owns responsibilities an API would have. Of course, the team does more than that, and Claude Managed Agents is one case we cover here.

The platform layer is being migrated from Python to Rust. Originally, this layer was written for Python for the “usual” reasons at AI companies: it’s a convenient language and AI researchers use Python already, which enables quick iteration. But Python is single-threaded, and at scale, when the API is under high load, it’s not as performant as Rust.

Harness infrastructure demand

The project came together due to customers wanting their own “harness infrastructure”, says Katelyn:

“We started with a model where you get an API to define an agent, then you get an API to start a session with an agent. The reality of what the world wants and needs right now is people running their own infrastructure. So, we started to build a self-hosted sandbox.

But then, what we started to hear from lots of customers is that they’re trying to hack harnesses together, running their own “harness infrastructure,” and this gave us the idea for Claude Managed Agents.”

The largest part: planning

In this project, Katelyn said the single biggest matter was planning:

“There are products you can jump straight to prototyping, but then there are ones where you need to start by architecting it properly. For example, if we build a TypeScript CLI – which is pretty trivial for what needs to be built – we could go straight to prototyping. But with Claude Managed Agents, we needed to first figure out what we are doing.

Of course, we did some upfront prototyping for Managed Agents: hacking and spiking things. But prototyping itself was more about understanding the requirements.

Our planning process looked more like a typical pre-AI planning process. You know how every team has the project, where everyone comes up with some version of the same idea and people keep floating and circling it around until you finally do it? Managed Agents was this for our team. When we started the project, we had documents dating back up to two years about ideas and suggestions.

Post-planning, when the project officially kicked off, a PRD (product requirements document) was created:

“In the end, it was the Product Manager and the Tech Lead on our API Agents team who decided to pull the trigger and kick off this project. We’d get in a room, go through it, and get aligned. But it wasn’t just us: we’d have to align with teams around the business, other cloud providers, and other engineering teams. For example, we have a sandboxing team inside of the Platform org: and so this team was consulted on the design of Managed Agents, given this product would spawn a lot of sandboxes.

Just like before, we had a PRD, it was a Google Doc. We used a Google Doc because we needed to coordinate all interested people. This has not gone away.

Similarly, my product counterpart and I run product reviews.”

Some processes from before AI, like the PRD, are still useful in complex projects today, for getting large groups of people on the same page.

Build for an internal customer first

With planning complete, the team decided to do a “spike” and stress-test the idea and architecture, by building the backend of Claude Code on the web. Remote execution of code with an agent harness was a similarly shaped problem to the one they wanted to solve for customers. The thinking was to start by solving it for the Claude Code team before tackling it in a more generic way for customers.

Internal teams are more fluid than before AI. Katelyn: