Anthropic lets AI agents independently develop C compiler

Anthropic recently highlighted how far autonomous AI systems have come through an ambitious internal experiment. In this project, sixteen AI agents collaborated to build a C compiler largely on their own—an outcome that reflects both meaningful technological advancement and notable constraints.

The timing of the experiment is significant. As major AI companies increasingly invest in agent-based systems, both Anthropic and OpenAI have rolled out new tools designed to support multi-agent workflows. According to Ars Technica, the release of these findings aligns closely with that broader industry push.

For the experiment, sixteen agents powered by Claude Opus 4.6 were assigned the task of creating a C compiler in Rust from the ground up. After defining the objective, human overseers stepped back, allowing the agents to operate independently. They collaborated through a shared Git repository, without a single coordinating agent directing the process.

To support this setup, Anthropic built a custom technical framework. Each agent ran inside its own Docker container and operated in a continuous loop, automatically launching a new session after completing assigned work. Coordination was handled through simple lock files within the repository, helping the agents avoid interfering with each other’s tasks.

Over nearly two weeks, the system generated around two thousand Claude Code sessions. During that period, roughly two billion input tokens were processed and about 140 million output tokens were produced, resulting in API expenses close to $20,000. The final product was a compiler comprising approximately 100,000 lines of code.

Anthropic reports that the compiler was capable of handling real-world software builds. It successfully compiled a bootable Linux 6.9 kernel for x86, ARM, and RISC-V architectures. It also managed to build major projects such as PostgreSQL, SQLite, Redis, FFmpeg, and QEMU. On the GCC torture test suite, the compiler achieved a success rate of about 99 percent. As a final informal demonstration, it even compiled and ran the game Doom.

Despite these achievements, outside observers have questioned how autonomous the process truly was. While the agents wrote the code independently, substantial human effort went into preparing the environment. Ars Technica notes that much of the complexity lay in setting up testing frameworks, CI pipelines, and feedback systems tailored to the known limitations of large language models.

Anthropic stresses that the compiler was created in isolation from external influences. The agents had no internet access during development and relied solely on the Rust standard library. For this reason, the company describes the project as a “clean-room” implementation.

That characterization, however, has sparked debate. Although the development environment was isolated, the underlying model had been pre-trained on vast amounts of publicly available source code, which likely included existing C compilers and related tools. Critics argue that this pretraining complicates the traditional meaning of “clean room” in software engineering.

As the codebase expanded toward 100,000 lines, the system began encountering challenges common to large-scale software projects. Fixes and new features increasingly introduced regressions, breaking previously working components. This pattern—familiar in human-led development—also emerged in this autonomous AI setting, suggesting there may be practical limits to how far current agent-based systems can scale.

Anthropic has released the full source code publicly and frames the initiative as a research effort. The project demonstrates the growing capabilities of multi-agent AI systems, while also highlighting the operational and structural boundaries that still shape large-scale autonomous software development today.

Voice Of Osiz

At Osiz, we see Anthropic’s autonomous C compiler experiment as a strong signal of where AI-driven development is heading. Multi-agent systems are no longer theoretical concepts; they are actively building complex software with minimal human intervention. This milestone highlights how intelligent agents can collaborate, automate workflows, and accelerate large-scale engineering tasks. At the same time, the experiment reminds us that structured environments, testing pipelines, and human-defined frameworks still play a crucial role. As an AI development company, Osiz focuses on blending autonomous AI capabilities with robust architecture and real-world business logic. We believe the future of software development will combine human expertise with coordinated AI agents for faster, smarter, and more reliable outcomes. Innovations like this reinforce our commitment to building next-generation AI solutions that transform how software is designed, tested, and delivered.

Source: Techzine