Anthropic’s Claude AI Agents Build a C Compiler From Scratch, Signalling a New Era for Autonomous Software Development
Just days after unsettling the SaaS industry with its collaborative AI system Claude Cowork, Anthropic has unveiled another breakthrough that is turning heads across the tech world. The company revealed that its newest model, Claude Opus 4.6, successfully built a complete C compiler entirely from scratch — not with one AI, but with a coordinated team of 16 AI agents working together.
The achievement stems from an internal experiment led by Nicholas Carlini, where multiple Claude agents were deployed simultaneously to tackle one of programming’s most demanding challenges. Their task: develop a Rust-based C compiler capable of compiling the Linux kernel.
Over the course of two weeks, these AI agents generated nearly 100,000 lines of code with minimal human oversight. The process unfolded across roughly 2,000 independent sessions and cost around $20,000 in API usage. Remarkably, the system worked without internet access. By the end of the experiment, the compiler could produce a bootable Linux 6.9 build across x86, ARM, and RISC-V architectures.
Claude Opus 4.6 introduces a new “agent teams” capability, allowing several AI instances to divide responsibilities and collaborate on a shared objective. Instead of a single model juggling everything, each agent focuses on specific subtasks, improving speed and efficiency while reducing bottlenecks.
The announcement sparked disbelief and excitement across the tech community. Reacting to the news, Derya Unutmaz, a professor at The Jackson Laboratory, wrote, “You got to be kidding me!”
The reaction is understandable. Building a C compiler is considered a notoriously complex engineering challenge. Compilers translate human-readable C code into machine instructions that processors can execute — a task that requires deep systems knowledge and precise optimization. Even seasoned developers often find such projects daunting.
Anthropic’s approach relied on a parallel workflow. Each Claude agent operated in its own isolated container while coordinating through a simple synchronization process. This setup helped distribute work effectively and manage conflicts between code contributions.
Performance tests showed promising results. The AI-built compiler cleared 99 per cent of the GCC torture test suite and even compiled and ran the classic game Doom. Still, the system isn’t perfect. It lacks a 16-bit x86 backend needed for certain Linux boot processes, depends on GCC during some stages, and trails established compilers in efficiency.
Carlini emphasized that strong testing frameworks were key to progress. He explained that sustained progress required "extremely high-quality tests" and continuous integration pipelines to ensure that new commits would not break existing code. Writing clear verifier scripts and maintaining up-to-date documentation enabled Claude agents to self-orient and recover from context loss between tasks—a common challenge with current language models.
Beyond the technical milestone, the experiment hints at a future where teams of AI agents could independently handle large-scale software development, potentially reshaping how products are built — and why some SaaS companies are beginning to feel the pressure.