Why I am writing this
This is the third piece in an accidental series about convergent evolution in agent tooling, and I think it is the most useful one, because this time the two systems being compared are not merely neighbours in the same field, they are the same species of thing: governance systems for AI coding agents, built in the same quarter, by people who have never spoken, with overlapping mechanisms and almost perfectly complementary blind spots. In the first article I described my DAG TOML stack, plans as machine-checkable claims with validators and a fleet control plane behind them, and in the second I compared two orchestrators. This one is about dgov by James H. Gearon, which describes itself as a "deterministic kernel for multi-agent orchestration via git worktrees". I should be straight about my method: I did not read the source line by line myself. I had my agents clone it and do the close reading (roughly 20,000 lines of Python across 70 modules, with 70 test files and a benchmarks document) and I worked from their structured analysis, the project's own documentation and the schema excerpts they pulled, which, given the subject of this article, feels less like a shortcut and more like a demonstration.
