AI is generating more code than ever

But we still version control only the output, not the reasoning

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