Darshandagaa goes after the part most multi-agent tutorials skip: making the run reproducible. Five LangGraph workers each write into their own subtree on one shared Cloud Volume, the supervisor merges through the mount-free API, and the snapshot at the end bakes the run's temperature and prompt variant into its message. Because every run lives on the same timeline, diffing run 17 against run 18 needs no restore, and fork() pins a fresh filesystem to any old snapshot to reproduce a run on a clean machine. The gotchas section is the part to keep: poll the mount before writing, stagger concurrent session starts, and mount under the user's home. No commits, no locks, and no agent ever learns Git.
We didn’t write this one — it’s Darshandagaa’s piece, published on Towards AI. The note above is ours; the full article is theirs.