Should you use Git AI?
Use Git AI for a pilot when you need to trace committed lines back to supported coding agents. Before making its reports part of a policy, check that known agent edits retain the right labels through your actual repository operations. Our run on October 9, 2026, at commit 0670e7e established a successful source build and an unsuccessful test-command outcome in our sandbox. It did not establish complete attribution in a working team's environment.
What our sandbox recorded
The harness recorded dependency installation as successful in 10 seconds and the build as successful in 339 seconds. The build log identifies git-ai v1.7.6. The cargo test step took 243 seconds, exited 101 and did not time out.
The stored parser summaries contain 5,232 passed, 2 failed and 5,234 total. The attached evidence warns that legacy output repeats Cargo summaries and may double-count suites. Those stored values remain part of the record, but unique and exhaustive counts have not been verified. We therefore draw no pass-rate or coverage conclusion from them.
The raw tail separately identifies test_graphite_style_multi_commit_single_update_ref as a failing case. Its Git executable rejected the merge-base option used by git merge-tree. That is evidence of a command compatibility problem in this sandbox; the excerpt does not demonstrate an attribution mismatch. It also does not identify a second distinct failure corresponding to the stored aggregate. Two delayed trace-replay cases in the displayed suite were ignored because of stated stock trace2 limitations. Ignored cases are not failures.
Our harness records step status, exit code, elapsed time and parsed test summaries, alongside log tails. The testing methodology explains that provenance model and its limits. The supplied October 9 record is the evidence for this run; the methodology page's later revision does not establish which procedure version was used then. No new execution was performed for this correction.
We did not separately exercise a real coding-agent session, native installers, hosted CI actions or a production deployment. No audit result is attached, and scanned-package coverage is unknown. A successful build is not a security assessment or proof that those workflows work.
How attribution reaches a commit
Git AI stores agent-reported line attribution in Git Notes. The README at the measured commit explains that coding agents call checkpoints when writing code or modifying files through Bash. On commit, Git AI associates lines with the reported agent, model and session.
This gives reviewers a record of reported activity rather than an authorship guess based on code style. Its usefulness depends on the relevant agent reporting the edits correctly. The README describes AI-aware blame and statistics for generated versus committed lines, accepted output and human overrides. Our run did not independently validate those statistics against a known workload.
Check repository operations before rollout
The README lists support for operations including rebase, cherry-pick, stash, squash merges and linked worktrees. These are documented capabilities, not a claim that our run verified every operation.
The README says attribution does not follow files moved with git mv. It also lists bulk history rewrites and object replacements as unsupported. For agents editing multiple repositories, Bash attribution requires each command's working directory to be inside the affected repository.
Hosted squash and rebase merges require Git AI for Teams or the open source CI actions to preserve attribution. Include that server-side step in a pilot rather than checking developer branches alone.
A useful acceptance check is to have each intended agent create a file and edit an existing one, then inspect attribution after committing and after the repository operations your team uses. Compare the labels with those known edits. This is a proposed next step, not work completed in our sandbox.
Coding assistants and Git clients answer different questions
Aider automatically commits its changes with commit messages. Choose it when you need a coding assistant whose edits you can inspect through ordinary Git history. Its documented commit behavior does not establish a replacement for Git AI's line-level attribution across supported agents.
GitButler supports stacked and parallel branches. Choose it when organizing concurrent changes is the immediate problem. Its Git interface addresses a different need from agent attribution and may be a complement; our run did not test interoperability between the tools.
How to read the ratings
The ratings are editorial judgments on a 1-to-5 scale, not measurements of adoption, security or attribution accuracy. A 1 means the supplied evidence shows substantial obstacles in the dimension; 2 means material gaps; 3 means a usable basis with significant qualification; 4 means strong supporting evidence with named limits; and 5 means unusually complete evidence for that specific dimension. Unknown dimensions should remain unassessed.
Setup ease concerns the recorded source setup, not native installer usability. Documentation concerns the supplied README's explanation of the mechanism and boundaries. Community concerns visible contributor participation in the supplied release extract, not user adoption or support responsiveness. Maturity concerns documented workflow breadth and release work, with the unsuccessful sandbox test outcome remaining a limitation.