The Era of AI Agents Writing Code: Why 20-Year-Old 'Git' Infrastructure Has Hit a Wall

Imagine this: You wake up in the morning and tell an AI, “Implement all the website features I need today.” The AI agent starts working immediately. However, this agent works in a completely different way than a human. It saves (commits) code and leaves records for almost every tiny action. Just as we breathe, AI saves code.

But ‘Git’, the code management tool we have been using for over 20 years, cannot keep up with this speed of AI. It is like thousands of high-speed trains running all at once on a narrow bicycle path built for humans. To solve this, the Git infrastructure, the heart of software development, is being rewritten entirely.

Why Is This Important?

Software development is shifting from an era led by ‘humans’ to one led by ‘AI agents’. However, the Git we use is optimized for the way humans wrote and managed code 20 years ago. A human saving dozens of times a day is a completely different problem from an AI agent performing and saving thousands of tasks in just a few minutes.

If this problem is not solved, the work speed of AI agents will hit a physical infrastructure wall and come to a halt. If development infrastructure does not change, future smart development agents will not be able to reach their full potential. Therefore, this technical shift is a crucial key that determines how much faster and more stably the future software we will use can be built.

Simple Explanation: Why Is Current Git Slow?

The Git we have been using until now was thoroughly designed for ‘humans’. Ref 7 This includes the series of processes where a human writes code, divides branches, and requests reviews from colleagues (PRs).

But AI agents are different. They try to create checkpoints or save code for almost every task. Ref 1 In other words, they constantly attempt to write data within a dense work loop. Existing Git infrastructure suffers from major bottlenecks when processing this ‘high-frequency writing’.

To use a simple analogy, it is as if thousands of people are rushing into a revolving door that had no issues when one developer was passing through. Since the narrow door can only let one person through at a time, the system screams because it cannot process the data of thousands of AIs.

So, companies are looking for new methods. For example, ‘Cursor’, an AI-based code editor, developed its own hosting system called ‘Origin’. Ref 6 This system uses a ‘WAL (Write-Ahead Logging)’ method on top of S3 (cloud storage).

To explain this with another analogy, instead of going to the chef to request a dish every time an order is received at a restaurant, it is a method of quickly writing down the order (log) on a notepad first and cooking it all at once later. Thanks to this, it has become possible to leave records much faster and more stably.

Current Situation: Technology Preparing for a ‘35x’ Miracle

GitHub is currently redesigning a new Git repository structure suitable for this agent era from the ground up. Ref 3 This is because the existing infrastructure used by humans simply cannot handle the increasing activity of AI agents.

The surprising news is that tangible results are already emerging. According to GitHub’s initial internal tests, the newly designed architecture showed 35 times faster write performance than the previous method. Ref 3 The number 35 is not just a slight improvement, but a powerful will to break through the fundamental limits of infrastructure. The industry analyzes that GitHub intends to preempt the agent-centered development environment through such infrastructure redesign. Ref 2

What Will Happen in the Future?

We are now facing a new reality called ‘Agent-scale Software Development’. Ref 9 In the future, more development tools and platforms will modify their infrastructure so that AI agents can read and write code to their hearts’ content.

Not only will Git’s performance be enhanced, but an environment where more multi-agent systems can collaborate easily will be created. Ref 8 As users, you might witness AI implementing more complex and vast software in a very short time more often. The quiet innovation of development infrastructure is becoming the foundation that makes the AI services right in front of us smarter and faster.

MindTickleBytes’ AI Reporter Perspective

To the general public who are not developers, ‘Git’ might be an unfamiliar term. However, in this era where AI is changing our daily lives, changing Git—the way AI works—is a very important shift. Deep in the data centers we cannot see, the overhaul of infrastructure for AI agents to stack up code dozens of times faster than humans has already begun. This shows that technological advancement is not just staying in the functions we see on the surface, but is changing fundamentally from the base facilities.

References

1. Building Git infrastructure for agent-scale development
2. GitHub Is Building Git Infrastructure for Agent-Scale Development
3. So long, Spokes: GitHub rewrites storage to restore reliability, just in time for agentic hordes
4. Startup Entire Launches Distributed Git Network for the…

5. [Cursor Origin: How Its New Git Hosting Actually Works

   explainx.ai](https://www.explainx.ai/blog/cursor-origin-git-hosting-block-buzz-projects-august-2026)

6. [GitHub’s Scale: Mind Boggling, Alternatives Limited

   LinkedIn](https://www.linkedin.com/posts/atharvapandey_origin-is-hosted-on-github-quite-funny-activity-7497924180622004224-p_BB)

7. n8n Just Made MultiAgent AI Way Easier: New AI Agent… - YouTube
8. GitHubが「AIエージェント時代」を見据えて、Git… - Qiita
9. AI Workflow Automation Platform - n8n