Git built for agents, not humans
Entire is a distributed Git network designed for AI coding agents, mirroring GitHub repositories into regional cells so fleets of automated coders can clone, fetch, and push at high volume without overwhelming a single centralized host or slowing down human developers.
The key shift is simple: Git infrastructure is finally being rebuilt for machines that read and write code all day. Entire, founded by former GitHub chief executive Thomas Dohmke, has opened a waitlisted preview of a regional Git hosting network tuned for AI coding agents. Developers authorize an app, mirror a GitHub repository, and point agents at an entire:// remote in a chosen region. The system uses a global control plane for identity and placement and regional data planes for Git object storage, effectively forming a distributed Git network with active preview regions across multiple continents.
This is not a minor optimization; it is an opinionated bet that the future of version control is high-frequency, automated traffic, not occasional human collaboration. If that bet is right, centralized Git hosting will look as dated as single-node databases in a world of microservices.
Why GitHub’s model is cracking under AI load
Agentic development has changed repository traffic patterns from gentle waves to constant storms. A human might clone once and pull a few times a day, but an AI fleet clones, inspects, checkpoints, and pushes across many parallel sessions. GitHub itself has acknowledged that agentic workflows have accelerated sharply since late 2025 and forced it to plan for far higher scale, with separate reporting tying this pressure to reliability incidents and cloud-capacity questions.
Recent outages on centralized, US-based Git servers during the rise of “vibe coding” and coding agents show the weakness of a single-region, single-host model. When the core host fails, every developer and agent stalls. Entire’s timing is therefore no coincidence. It is built on the premise that repeated AI-driven reads and writes are now the dominant load, and that this load belongs on agentic infrastructure, not on platforms optimized for human-driven pull requests. According to one report, “GitHub has suffered many an outage in recent months as vibe coding has exploded and coding agents have overrun the centralized platform and its US-based servers.”
The message for enterprises is blunt: treating AI coding agents as a sidecar to legacy tooling is no longer safe. They are now primary traffic, and infrastructure that ignores this will keep breaking.
Inside Entire’s distributed Git network for AI coding agents
Entire’s preview is intentionally conservative in scope: it mirrors, rather than migrates, GitHub repositories. The mirror is a synced, read-optimized copy; code and governance stay on GitHub, while agents clone and fetch from regional Entire mirrors. This lets Entire absorb heavy read traffic by serving clones, fetches, refs, commits, files, diffs, and merge bases from a nearby regional cell.
Under the hood, the network is Git-compatible, with a global control plane and regional data planes for object storage, forming a decentralized Git network built for agents. Vendors claim serious scale: company-run benchmarks report up to 570,000 clones per hour, 2.1 million pushes per hour (about 586 per second), and 1.7 million mixed clone-and-push operations per hour (around 470 per second). Those numbers are not independent proof, but they set expectations.
Practically, this looks less like a pure GitHub alternative and more like an AI offload layer. The launch should be read as a promising infrastructure preview, not a full replacement for established Git hosts. Developers keep branch protection and checks on GitHub, while agents gain low-latency, rate-limit-free access to mirrored repos. In other words, Entire is a pressure valve for AI coding agents, not yet a new home for all your code.
Agentic infrastructure roadmap: from mirrors to native hosting
Entire’s bigger ambition is to evolve from mirror network to full agentic infrastructure. The company plans native public and private repositories, self-hosted nodes, an open-source Git backend, tamper-evident branch history, policy-as-code branch protection, CI/CD features, and broader decentralized operation. These are roadmap items, not shipping features. Entire’s own documentation notes that the strongest write-throughput path relies on Entire-native or unmirrored branches, which do not yet exist for everyone.
The more provocative piece is how Entire ties Git to AI memory. It stores agent sessions, prompts, tool calls, and checkpoints alongside repository history so reviewers can inspect why code changed, not only what changed. Redacted transcripts and checkpoint metadata live in dedicated branches, though the company warns that redaction is best-effort and sensitive workloads should stay private.
If delivered, this roadmap turns version control into an audit trail of autonomous behavior. That is exactly what enterprises will need as regulators and security teams ask not only “who merged this?” but “which AI agent decided this change was safe?” Entire will have a stronger case once outside developers reproduce the benchmark results and see these native hosting and self-hosting features land as promised.
Glint and the rise of AI-native developer workspaces
Entire is not the only player betting on agent-first tooling. Glint Software has opened the public beta of a native desktop workspace that brings multi-repo Git management, terminals, worktrees, and agentic AI coding into one repository-aware environment. Instead of bolting chat windows onto editors built for single files, Glint treats AI as a first-class participant in the workspace.
Glint’s application lets Git worktrees, issues, pull requests, terminals, and an AI agent called Glint Assist share the same context. It also works across multiple providers, including GitHub, GitLab, and Bitbucket, instead of requiring a single host. As teams use AI agents to refactor code and ship features, existing tools have not kept pace; extensions lose context when branches change or multiple repositories are involved. Glint’s answer is to centralize that context in one AI-aware workspace.
The pattern is clear: where Entire attacks the backend with a distributed Git network for AI coding agents, Glint attacks the frontend with an AI-native workspace. Both reject the idea that legacy IDEs and Git hosts can simply add a sidebar for agents. They are building end-to-end environments where agents and humans share the same multi-repo, multi-branch reality.
What this shift means for enterprise development tools
Taken together, Entire and Glint mark the start of a new category: agentic infrastructure. AI agents make Git hosting less like occasional human collaboration and more like high-frequency distributed systems infrastructure. Entire’s preview is a credible attempt to solve a real agent-era bottleneck: high-volume repository reads from many automated coding sessions. Glint tackles a parallel gap on the desktop, where multi-repo, AI-aware workflows finally get a home.
For enterprises, the strategic question is no longer whether to try AI coding agents, but where to run them. Relying only on a centralized GitHub alternative misses the point; the future stack will pair distributed Git networks, AI-native workspaces, and policy-aware pipelines tuned for autonomous traffic. Tooling that treats agents as background helpers will be outcompeted by systems that treat them as core users.
The conclusion is uncomfortable for incumbents: the era of “Git plus plugins” is ending. In its place, we will see infrastructure that assumes agents constantly read, write, and reason over code. Enterprises that adopt this early will gain reliability and auditability; those that cling to human-only tooling will keep discovering that their version control system is now the bottleneck.
