Building a C compiler with a team of parallel Claudes \ Anthropic

Anthropic’s Claude Agent Teams Build a 100,000-Line C Compiler from Scratch

In a groundbreaking demonstration of autonomous AI development, Anthropic researcher Nicholas Carlini has revealed how “agent teams” of Claude instances can collaboratively build complex software systems with minimal human intervention. The project—a Rust-based C compiler capable of building the Linux kernel—represents a significant milestone in AI-assisted programming.

How Agent Teams Work

Traditional AI coding assistants like Claude Code require constant human oversight. When given a complex task, they typically pause after completing portions, waiting for clarification or direction. Carlini’s agent team approach eliminates this limitation by creating a self-sustaining loop where multiple Claude instances work in parallel on shared codebases.

The system operates through a simple bash loop that continuously spawns new Claude sessions:

bash
while true; do
COMMIT=$(git rev-parse –short=6 HEAD)
LOGFILE=”agentlogs/agent${COMMIT}.log”
claude –dangerously-skip-permissions \
-p “$(cat AGENT_PROMPT.md)” \
–model claude-opus-X-Y &> “$LOGFILE”
done

Each agent locks specific tasks by creating files in a current_tasks/ directory, preventing duplicate work. When conflicts arise, Git’s synchronization forces agents to pick different tasks, while Claude’s intelligence handles merge conflicts automatically.

The 16-Agent Compiler Project

To test the limits of this approach, Carlini deployed 16 Claude agents to build a C compiler from scratch. Over nearly 2,000 sessions spanning two weeks, the team consumed 2 billion input tokens and generated 140 million output tokens, costing approximately $20,000 in API fees.

The resulting compiler—available on GitHub—represents a 100,000-line clean-room implementation that can build Linux 6.9 on x86, ARM, and RISC-V architectures. It successfully compiles major open-source projects including QEMU, FFmpeg, SQLite, PostgreSQL, and Redis, achieving a 99% pass rate on compiler test suites.

Key Lessons from Autonomous Development

Test Quality is Paramount

The success of agent teams hinges on test harness quality. Since Claude works autonomously, imperfect tests lead to solving wrong problems. Carlini developed extensive test suites, including continuous integration pipelines that prevent new commits from breaking existing functionality.

Design for Claude’s Limitations

Carlini had to think like an AI, accounting for context window pollution and time blindness. Test harnesses now print minimal output, log important information to easily searchable files, and include deterministic sampling options that let agents identify regressions without spending hours on full test runs.

Parallelism Requires Smart Task Division

When the compiler reached 99% test pass rates, agents could work on different open-source projects simultaneously. However, compiling the Linux kernel presented a unique challenge—it’s essentially one massive task. The solution involved using GCC as a “known-good compiler oracle,” allowing agents to work on different files in parallel while comparing results.

Specialization Unlocks Efficiency

Multiple agents enabled specialization: one focused on eliminating duplicate code, another on compiler performance, a third on code quality and documentation, and others on specific subsystems. This division of labor dramatically accelerated development.

The Compiler’s Capabilities and Limitations

The agent-built compiler demonstrates remarkable capabilities:

• Builds bootable Linux 6.9 on three major architectures
• Compiles complex projects like Doom (passing the developer’s ultimate litmus test)
• Achieves 99% pass rates on major compiler test suites
• Operates as a clean-room implementation with no external dependencies

However, significant limitations remain:

• Requires GCC for 16-bit x86 compilation needed to boot into real mode
• Lacks its own assembler and linker (still somewhat buggy)
• Doesn’t yet serve as a complete drop-in replacement for production compilers
• Generates less efficient code than GCC with optimizations disabled
• Produces Rust code quality below expert human standards

The Future of Autonomous Software Development

This experiment represents more than just a technical achievement—it signals a fundamental shift in how software can be developed. While current tools like Claude Code pair humans with AI for short sessions, agent teams enable autonomous execution of entire complex projects.

The implications are profound. Projects that once required large teams of specialized developers can now be tackled by coordinated AI agents, dramatically reducing development costs and timelines. However, this autonomy comes with risks. Without human oversight, ensuring code quality and security becomes challenging.

Carlini acknowledges these concerns, noting that while the positive applications likely outweigh negatives, we’re entering uncharted territory that requires new safety strategies. The rapid advancement of both AI models and development scaffolds means we must prepare for a world where AI can write enormous amounts of code independently.

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This demonstration of agent teams represents a pivotal moment in AI development, showing that autonomous software creation is not just possible but practical. As models continue to improve, we can expect even more ambitious projects to emerge from these collaborative AI systems, fundamentally reshaping how we think about software development.

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