Totally correct on the burden of proof here. Agents DO know git extremely well. There’s a huge amount of git in model training data, and anything new starts behind because you have to teach the model what it is, what commands to run, and where the sharp edges are. For us “for agents” does not mean “new syntax that we hope agents can read docs for.”

The thing we’re trying to optimize is not whether an agent can remember the command. It’s the runtime shape of agent-driven development.

When an agent drives a VCS through a captured terminal, things that are tolerable for humans become direct costs: clone/setup time, worktree setup, full status output, huge diffs, branch cleanup, interactive prompts, shared-checkout mutation, repeated preflight checks. Those costs show up as wall time, bytes over the wire, transcript tokens, and recovery steps.

So the Oak bet is narrower than “agents can’t use git.” They can. The bet is that if you assume branch-per-agent workflows, lots of parallel sandboxes, large repos, and non-interactive command execution, the VCS interface should have different defaults if you want to optimize for shipping speed and efficiency of token usage. If you're already going fast enough and not running out of tokens - then using oak seems pretty silly.

People do not need to ditch git to try Oak out. One workflow we care about is letting agents work in Oak where the agent-specific costs matter, then exporting back to git for the human review, CI, release, or compliance workflows.

Totally agree this should be provable and benchmarked. The homepage has Oak vs Git numbers because we do not want “for agents” to just be vibes. We’re measuring transcript bytes, estimated tokens, tool calls, wall time, large diff/status behavior, and contention in agent-style workflows. We’re also working on the benchmarks repo in the open: https://oak.space/oak/benchmarks

The exciting part to me is that we can already improve on tokens and timing despite starting with the model-prior deficit you’re describing. If we can win on measured agent workflows while git still has the advantage of being deeply baked into the models, I’m incredibly bullish on where Oak can get to as the tool and the ecosystem matures.

Longer term, if Oak proves useful and sticks around, future frontier models will likely have more Oak examples in training data, which lowers the upfront learning tax for an extra boost.