Imagine writing code with a brilliant, hyper-fast assistant who types a hundred lines a minute but never explains their thought process. When things break, you are left cleaning up the mess without knowing why the assistant made those choices. This is the reality for developers using modern AI coding agents today.
Welcome to the era of AI-native version control. For over a decade, Git has been the undisputed king of saving and tracking our code. But as artificial intelligence steps up from being a simple auto-complete tool to an active coding partner, Git’s limitations are showing.
Enter Entire.io. Built by former GitHub CEO Thomas Dohmke, Entire.io is not here to replace Git. Instead, it acts as a “semantic reasoning layer” to capture the context behind AI-generated code.
In this comprehensive guide, we will explore why the software development world is shifting, how AI-native version control works, and how tools like Entire.io are bridging the gap between human intent and AI execution.
Table of Contents
1. The Big Problem: Why Git Struggles with AI
To understand why we need AI-native version control, we first need to look at how we have been managing code up until now.
For the past twenty years, Git has been the standard. Created in 2005, it gave developers a decentralized way to work. Everyone could have a copy of the code, work offline, and merge their changes later.
Git tracks what changed. If you delete five lines and add ten, Git takes a snapshot of that difference. However, Git was built for humans typing on keyboards. It was never built for AI agents like Claude Code or Google Gemini generating massive chunks of code in seconds.
The “What” vs. “Why” Dilemma
Git is incredible at tracking the final result, but it completely ignores the journey.
Think about a standard coding session:
- You spend three hours researching a bug.
- You try four different solutions that fail.
- You read three pieces of documentation.
- Finally, you write five lines of code that fix the issue.
When you save (or “commit”) this to Git, those three hours of struggle and learning are erased. Git only saves the final five lines. The only place you can explain why you wrote it is in a tiny text box called a “commit message.”
When an AI agent writes code, this problem multiplies. The AI might test ten different paths in a matter of seconds. If we only save the final output, we create a dangerous “Provenance Gap.” This means the code is completely disconnected from the reason it was created.
2. Enter Entire.io: The Pioneer of AI-Native Version Control
Entire.io (or simply “Entire”) was built to solve this exact problem. It is a brand-new type of developer tool designed to track not just the code, but the conversation and context that created the code.
Entire.io does not replace Git. Your code still lives in Git. Instead, Entire acts as a middle layer. It sits between the AI agent and your Git repository, recording the “thought process” of the AI while Git records the final files.
The Checkpoint vs. The Git Commit
In Git, the core way to save your work is a “Commit.” In Entire, the core way to save is a “Checkpoint.”
Here is a simple breakdown of how they compare:
| Feature | Standard Git Commit | Entire.io Checkpoint |
| What it saves | Snapshots of files and code changes. | The AI’s reasoning, your prompts, and transcripts. |
| How it triggers | Manually (you type git commit). | Automatically after the AI responds, or manually. |
| Detail Level | Broad. Usually covers a whole feature or bug fix. | Deep. Covers a single thought or conversational step. |
| Where it lives | In your main project history. | In a separate, hidden “shadow branch.” |
| Main Goal | Track what the code looks like now. | Track why the AI wrote the code that way. |
Shadow Branches: Keeping Your Main Code Clean
You might be wondering: “If Entire is saving massive transcripts of my chat with an AI, won’t that make my project file huge and slow?”
This is a great question. Git struggles with large, non-text files (like massive data logs). Entire solves this brilliantly using Shadow Branches.
Instead of stuffing all this AI context into your main codebase, Entire creates an invisible, parallel timeline (called entire/checkpoints/v1). Your main branch stays perfectly clean and fast. The shadow branch holds all the heavy AI logs.
Entire links the two together by adding a tiny, hidden ID tag to your normal Git commit.
(Internal Link Suggestion: You may also read our guide on “Understanding Git Branches for Beginners” to grasp how branching works.)
3. Real-World Examples: The “Save Button” vs. The “Documentary”
Let’s translate this complex tech into a simple English analogy. Imagine you are writing a novel.
The Git Way (The Standard Save Button)
You write Chapter 1. You press “Save.”
Later, you decide the ending is too sad. You rewrite the last page and press “Save” again.
Git only keeps the old page and the new page. Ten years later, someone reading your history knows you changed the ending, but they don’t know why. Did your publisher hate it? Did you just change your mind? The intent is gone.
The Entire.io Way (The Documentary Crew)
Now imagine writing that same book with an AI co-author, while a documentary crew films you.
You tell your AI: “Make the ending happier. Have the hero survive.”
The AI thinks, suggests a new scene, and you accept it.
Entire.io saves the new pages (just like Git). But it also keeps the documentary tape. It saves your prompt (“Make it happier”) and the AI’s internal logic. Anyone looking at the history can see exactly why the changes were made.
4. Use Cases: How Entire.io Rescues Developers
How does AI-native version control actually help you in your day-to-day coding? Let’s look at two massive use cases.
Use Case 1: The “AI Rabbit Hole” Time Machine
AI agents are smart, but they can easily get confused and go down “rabbit holes.” They might try to fix a small bug and end up breaking your whole project.
If you use Git, fixing this mess is a nightmare. You usually have to delete all your recent work and start over from your last manual save point.
Entire.io acts like a DVR for your code. Because it creates a Checkpoint after every single AI thought, you can just type entire rewind. This instantly rewinds your project back to the exact minute before the AI messed up. You keep your good progress and only erase the AI’s mistakes.
Use Case 2: Solving the Code Review Bottleneck
The biggest bottleneck in software today isn’t writing code; it’s reviewing it.
When a human writes code, they can explain it to their team. When an AI generates 500 lines of complex code in seconds, the human reviewer is left staring at a massive, confusing wall of text.
Entire acts like a Black Box Flight Recorder. The reviewer can click the Checkpoint ID and see exactly what prompt the developer used. They can read the AI’s plan. This builds trust, allowing teams to approve code safely and much faster.
5. Step-by-Step: Getting Started with Entire.io
Ready to bring AI-native version control into your workflow? Here is a simple, beginner-friendly guide to getting started.
Step 1: Install the Entire CLI
Entire works right in your computer’s terminal. If you use a Mac, you can install it using Homebrew. Just open your terminal and type:
brew tap entireio/tap
brew install entire
Step 2: Initialize Your Project
Navigate to the folder where your existing Git project lives. Type:
entire enable
This command works magic behind the scenes. It sets up the hidden “Shadow Branches” and prepares Entire to listen to your AI agents.
Step 3: Choose Your AI Agent
Entire currently supports powerful agents like Claude Code and Google Gemini. You can specify which one you are using by adding a flag, like so:
entire enable --agent claude-code
Step 4: Code as Normal
Start talking to your AI agent. Ask it to build features or fix bugs. Entire will quietly run in the background, saving Checkpoints of the conversation.
Step 5: Commit Your Work
When you are happy with the code, just run your normal Git commit:
git commit -m "Fixed the login bug"
Entire will automatically attach the AI’s “thought process” history to that commit!
6. Actionable Tips and Best Practices
To get the most out of AI-native version control, follow these best practices:
- Use the Right Strategy: Entire offers a “Manual-Commit” mode (which feels like normal Git) and an “Auto-Commit” mode (which saves everything constantly). Start with Manual-Commit so you don’t get overwhelmed with data.
- Write Clear Initial Prompts: Because Entire saves your prompts forever, treat your prompts like official project documentation. Be clear, specific, and detailed.
- Don’t Abandon Git Best Practices: Entire augments Git; it doesn’t replace it. You should still write clear, concise Git commit messages for your human team members.
- Use the Doctor Command: If your Git history and your Entire history ever get out of sync (maybe you deleted some branches manually), just run
entire doctor. This built-in tool will scan your project and fix broken links automatically.
(External Link Suggestion: Check out the official Git documentation on best practices for committing code.)
7. Frequently Asked Questions (FAQs)
1. Do I need to stop using Git to use Entire.io?
Not at all! Entire.io is built on top of Git. Your files are still saved using standard Git commands. Entire just runs alongside it to save the AI conversations.
2. Will Entire.io make my GitHub repository massive and slow?
No. Entire uses a clever trick called “Shadow Branching.” All the heavy AI logs are saved in an invisible, separate branch. Your main code stays light, fast, and clean.
3. Which AI agents work with Entire.io?
Currently, the system is highly optimized for autonomous coding agents like Claude Code and Google Gemini CLI.
4. What is a “Provenance Gap”?
It is the missing link between how a piece of code was written and why it was written. AI agents write code so fast that the human intent gets lost. Entire.io bridges this gap.
5. How is entire rewind different from git reset?
git reset takes you back to your last major save point, which could be hours ago. entire rewind lets you step back through the AI’s exact conversational steps, minute by minute, without losing all your work.
6. Is AI-native version control only for big companies?
While large companies love it for security and compliance, solo developers benefit massively from the “rewind” feature to fix AI hallucinations and mistakes quickly.
7. How does my team review the AI logs?
When you commit code, Entire attaches a tiny ID to the commit. Your team can click that ID to open a web dashboard showing the entire prompt and AI reasoning history.
8. Can I use this for non-coding AI generation?
Currently, Entire is specifically designed to integrate with Git repositories and coding agents. It is strictly for software development life cycles.
9. What happens if I make a mistake while using Entire?
You can run the entire doctor command in your terminal. It acts as a troubleshooting tool to fix desynchronized files and clean up errors.
10. Is Entire a security risk for my code?
Entire actually improves security! By keeping a cryptographic “Chain of Custody,” your company can prove exactly who prompted the AI and why the code was written, which is great for compliance.
8. Conclusion & Key Takeaways
The way we build software is fundamentally changing. Generative AI is turning developers from “code typers” into “code directors.” As our tools evolve, our methods for tracking our work must evolve too.
Key Takeaways:
- Git tracks the what (the final code), but AI-native version control tracks the why (the context and prompts).
- Entire.io sits on top of Git, using “Checkpoints” to save AI transcripts without bloating your main codebase.
- Features like
entire rewindact as a time machine, saving developers from messy AI hallucinations. - By providing a “Black Box Flight Recorder” for AI, Entire builds trust and speeds up the code review process.
Next Steps
Are you ready to stop fighting with messy AI code and start tracking your context?
Would you like me to generate a checklist on how to prepare your current Git repositories for an AI-native workflow? Recommended Resource: You may also read our comprehensive guide on “How to Write Perfect Prompts for AI Coding Agents.”