I am working on solving the AI Code Provenance problem and I believe my repos may be the first that provides AI code provenance. See the following example:

https://github.com/gitsense/gsc-cli/blob/main/internal/cli/r...

Notice how the code block header attributes the model. The UUID can be traced to the conversation so everybody can tell exactly how the code came about. For this to work though, you need to use my chat app as it ensures you can't tamper with things if you are truly serious about AI code provenance.

I also have a lot more human-focused method which is part of my CLI tool.

https://github.com/gitsense/gsc-cli

I am currently looking at making pi (https://github.com/earendil-works/pi) support AI code provenance, but for now if you want a more structured way to capture what you have done in an agent session that can be used in code reviews and be carried forward as knowledge that lives inside your repository, I have

gsc lessons

The basic idea is, after you have finished chatting/working with the agent, you would work with it to identify lessons worth carrying forward. You can store your session if you want, but really, the lessons should be something that can help you review code better and to prevent future mistakes.

I have a real working example at

https://github.com/gitsense/smart-ripgrep

This is a fork of the BurntSushi/ripgrep repository. It shows how you can use lessons to learn from past design decisions.

We're working on it, thought it's all early. I'd love feedback: https://tern.sh

First product compares the code to the prompts and highlights places the agent made decisions you weren't involved in: https://tern.sh/docs/tours/

We just have hook that runs on git push that instructs Claude to ensure the PR description is up to date. Works well enough for us.