Git Is Cracking Under AI Agents Anthropic and OpenAI have each rebuilt source control infrastructure to handle AI-agent-driven change volume that conventional Git cannot sustain, Anthropic reported. Anthropic's daily Claude Code changes rose from roughly 2,500 to over 30,000 in four months, and its main branch history approached 100 GB with about 25 million CI checkouts daily; its replacement system, Scion, uses around 200 times less CPU and memory than its prior Git mirroring and recovered approximately 25% of CI CPU capacity. OpenAI kept Git's interface but built Pando, a Rust high-concurrency backend serving almost a petabyte of Git data daily, cutting median push latency by nearly 80% and going from kickoff to production in about 10 weeks. Agents are making Git’s bottlenecks impossible to ignore AI agents are generating Claude https://www.stork.ai/en/poe-claude Code at a pace that is exposing fundamental bottlenecks in conventional source control. The tension is clear: while agents can generate changes rapidly, the human-centric workflows for coordination, testing, and review are struggling to keep up. Consider the scale: Anthropic reported daily Claude Claude Code changes skyrocketing from approximately 2,500 to over 30,000 in just four months . OpenAI’s monorepo reliability sometimes dipped below 90% as its agents pushed changes. These are extreme cases, but they highlight a systemic challenge. Even at a smaller scale, developers using local agents face similar coordination problems. Agents sharing one working directory can overwrite files or disrupt each other’s tests, leading to more time spent resolving conflicts than writing new Claude Code. The infrastructure problems at Anthropic and OpenAI are massive, yet the underlying coordination problem is familiar to anyone running multiple coding agents on the same project. The core issue is that Git, designed for human developers working primarily offline, struggles with the constant, high-volume, and parallel demands of AI. OpenAI kept Git—and rebuilt what was behind it OpenAI took an opposite approach to Anthropic, rebuilding the backend while keeping Git’s familiar interface. Developers continue using standard git push and git fetch commands. This system, called Pando , is a custom, high-concurrency backend written in Rust, designed to scale with AI agent demands. The bottleneck was coordinating simultaneous updates across numerous branches. Pando resolves this by handling updates to different branches immediately, replacing monolithic ref locking with independent ref coordination. This allows agents to push to distinct branches concurrently, eliminating serialized bottlenecks that previously slowed all activity. Pando also caches Git pack files , preventing the server from regenerating identical data repeatedly for common CI job requests. This efficiency is critical as the system now serves almost a petabyte of Git data daily . OpenAI reports impressive results: median push latency fell by nearly 80% , making workflows significantly faster. The trade-off is that reads across multiple branches may not present one perfectly synchronized view of the repository. Pando went from kickoff to production in approximately 10 weeks, with its core designed by one engineer and largely implemented by Claude Claude Code agents. Anthropic asked whether agents need Git at all Anthropic, on the other hand, questioned Git's fundamental assumptions. Its team asked whether an offline-first, distributed version control system remained optimal when most clients were always-online AI agents and automated jobs. This led to Scion , a complete replacement for Git. Anthropic's main branch history was approaching 100 GB , with compressed working copies exceeding 25 GB. Its CI infrastructure performed roughly 25 million checkouts daily . These pressures highlighted the growing overhead of deep local repositories and Git’s decentralized model. Scion moved commits, merges, and history operations to a server-side system. Clients request only the files and data they need, eliminating the need for large local repositories. This architecture yielded significant efficiency gains. Anthropic reported Scion uses around 200 times less CPU and memory than its previous Git mirroring infrastructure. Some checkouts that once took minutes now complete in seconds, and CI recovered approximately 25% of its CPU capacity . To fully leverage Scion, Anthropic rebuilt its Claude Code review, Claude Code search, and CI tooling. For further technical details, see Redesigning source control at Anthropic https://www.anthropic.com/research/redesigning-source-control . Scion is not a general Git replacement, but a specialized solution for Anthropic's unique agent-driven workload. Enjoying this? Get one like it in your inbox each morning. one email a day · unsubscribe in two clicks · no third-party tracking Your agents need worktrees, not a new source-control system Pando and Scion represent diverging bets. OpenAI prioritizes Git compatibility, rebuilding the backend to handle petabyte-scale data and 30,000 daily changes without altering developer workflows. Anthropic’s Scion, conversely, is a clean-slate system tailored to its internal agent workloads, discarding Git’s offline-first assumptions for an always-online, server-authoritative model. Neither system is available for general use, and their impressive internal benchmarks e.g., Scion’s 200x CPU/memory efficiency or Pando’s 80% push latency reduction do not translate to laptop-scale gains. For most teams, Git remains robust. The immediate solution for local agent conflicts is git worktree add