Claude Code Is Making 'Backend Work' the Default!
Just moments ago, Claude Code creator Boris Cherny dropped a line on X: The next version of Claude Code will have sub-agents running in the background by default.
You can chat with Claude while letting sub-agents finish tasks in the background—want a specific agent to run in the foreground? Just tell Claude.
This single statement made many developers realize something instantly: Claude Code is evolving from a "Q&A dialog box" into a "workflow engine capable of managing multiple task lines simultaneously."
While you're still discussing architecture plans with Claude, sub-agents have already finished code refactoring, run tests, and opened PRs in the background.
You just need to glance at the results after you finish chatting.
Sounds like science fiction?
No, this is Boris Cherny's daily routine—he hasn't written a single line of code by hand in eight months, and on some days, he manages thousands or even tens of thousands of AI agents simultaneously.
"You're no longer the one writing prompts for Claude," he says, "another Claude writes the prompts."
Some netizens have mentioned they're already using this feature.
Others noted this feature is crucial—it prevents boredom while waiting for agents to process tasks and allows planning for the next steps.
Claude Code's Blazing Trajectory
Looking back at Claude Code's evolution over the past six months, "background sub-agents" are not a sudden gimmick but a natural progression.
Boris Cherny's design philosophy when creating Claude Code was: Not chatting, but infrastructure.
In April, Anthropic first made "scheduled tasks" an official capability of Claude Code: Routines.
You can package a prompt, a code repository, and a set of connectors into a fixed workflow, triggering it by the hour, overnight, weekly, or via API calls, GitHub events, or even external webhooks.
More importantly, it runs on Anthropic's hosted cloud infrastructure—close your laptop lid, and the agents keep working.
This means cron is back, hooks are back, but this time, what's scheduled isn't scripts but a group of AI workers that can read code, modify code, and open PRs.
The engineer's role has also changed: Before, you'd close your computer before bed and continue writing the next day; now, you deploy a batch of agents before bed and review a bunch of PRs in the morning.
At the end of May, Claude Code pushed this logic further: Dynamic workflows.
For tasks too large for a single dialog—like major migrations, full-library audits, or complex research—you just need to include "use a workflow" in the prompt or enable ultracode. Claude will generate an orchestration script for the current task, scheduling dozens to hundreds of sub-agents in the background to advance in stages, perform parallel cross-validation, and finally consolidate the results into a report or a batch of changes.
This isn't "one AI writing code" anymore; it's "one AI writing the script, and a group of AIs working according to it."
Now, the step of "background sub-agents running by default" essentially bundles all the above capabilities into an out-of-the-box default behavior: You no longer need to manually say 'run in the background'; it inherently runs in the background.
You only need to focus on what you should truly be doing—thinking about the next step.
One Engineer Becomes Three
How powerful is Claude Code? The most convincing evidence isn't demos but the story of Anthropic itself being "backfired."
On June 27, VentureBeat published a heavyweight article with a straightforward title: Claude Code Turned Every Engineer Into Three. Now Companies Need More Product Thinkers.
Anthropic recently told its growth team: Hire more product managers, not more engineers.
The reason is simple—Claude Code has tripled the effective output of engineering teams. A five-person team now delivers the work of fifteen to twenty people.
The bottleneck isn't in coding; it's in the people "deciding what code to write."
Traditionally, the product manager-to-engineer ratio was about 1:8. Now, with each engineer's daily output tripled, this ratio effectively becomes 1:20.
PMs can't assign work fast enough—engineers finish coding and sit waiting for requirements, a scene that's absurd to imagine.
Spotify's 20 Million Lines of Code Managed by Claude
The best demonstration of how powerful "background sub-agents" are comes from Spotify's real-world use.
Spotify's Vice President of Engineering, Niklas Gustavsson, shared some figures in an interview with Boris Cherny:
Spotify deploys to production about 4,500 times daily, 73% of pull requests are completed with AI assistance, and PR frequency has increased by over 75%.
His own daily workflow involves: simultaneously opening 5 to 10 Claude sessions, each corresponding to an independent git worktree, letting multiple agents work in parallel in the background, while he only focuses on reviewing diffs and making decisions.
All this happens in a super-monorepo with over 20 million lines of code.
Niklas admitted he was initially worried that with such a large codebase, agents would get lost. Surprisingly, it went smoothly—Claude can even "find inspiration" from other code in the repository and know how to write.
His advice to peers sounds unsexy but exceptionally practical: The more consistent the codebase and unified the toolchain, the better Claude performs within it.
If the same thing is written in ten different ways across the repository, Claude will also get confused. This mirrors the logic of improving human engineer efficiency over the past decade, just with AI as a new player now.
Even more interestingly, Spotify opened this capability to non-engineers.
They built an infrastructure allowing product managers, designers—anyone—to describe an idea in natural language, and Claude directly implements end-to-end prototypes in real mobile and backend code.
Niklas revealed that even Spotify's co-CEO has submitted his prototypes within it.
Ideas that previously required convincing an entire engineering team to validate can now be tested and run through in just an hour or two.
When 'Chat While Working' Becomes the Default Setting
Niklas said this traces back five or six years—back then, the team noticed the codebase was growing seven times faster than the number of engineers, forcing them to think ahead about "whether machines could maintain code for people."
He found that what he truly enjoyed was never the act of coding itself but the process of solving problems.
Now, he runs several agents in the background simultaneously, using the freed-up time to figure out what to do next, what to discuss with clients, and create more prototypes.
This恰好印证了 Boris Cherny's tweet: When background sub-agents become standard, "writing code" is no longer the engineer's most important task; "deciding what to do and judging if it's right" is.
When "chat while working" evolves from a developer's personal trick to the shared working method of a 2,900-person engineering team, AI programming tools have quietly shifted to a new scale—
Engineer output triples, but the scarcest resource is no longer people who can write code, but people who know what code to write.
References:
https://x.com/kimmonismus/status/2071667876415623534
https://venturebeat.com/infrastructure/claude-code-turned-every-engineer-into-three-now-companies-need-more-product-thinkers
https://x.com/ClaudeDevs/status/2071671418245492926?s=20
This article is from the WeChat public account "新智元", author: ASI启示录
