# AI is generating more code than ever


But we still version control only the output, not the reasoning  
  
[**Entire.io**](http://Entire.io) ([**https://entire.io/**](https://entire.io/)) is addressing that gap  
  
Their open source CLI, Entire, integrates with Git and captures full AI agent sessions alongside commits. These records, called Checkpoints, store prompts, transcripts, tool calls, and the resulting code changes  
  
So what is the real use of this data?  
  
• You can audit why a change was made  
• You can review intent, not just diffs  
• You can reproduce or refine past AI sessions  
• You create long term organizational memory around decisions  
  
Traditional Git answers what changed  
  
Entire’s approach answers why it changed  
  
As AI agents become real contributors to codebases, that distinction becomes critical