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.