What does this equation mean?

The two axes are not independent, and the coupling matters more than either axis alone. Picture agent-produced candidate changes arriving at an organization’s review-and-merge gate at rate $\lambda$ , while the organization’s verification stage — tests, evaluators, human reviewers — disposes of them at rate $\mu$ . Treated as a simple queue, it is stable only while $\rho = \frac{\lambda}{\mu} < 1$. Parallel agent sessions raise $\lambda$ directly; that part is already happening, as the “agent teams” and fan-out patterns in Claude Code’s own documentation for running many sessions at once make explicit [ 3 ] . What raises $\mu$ is the less obvious half, and it is exactly where the two axes touch: if evidence generated under one tool’s verification pass has to be regenerated from scratch every time a change crosses a tool or organizational boundary, the effective $\mu$ available at any single gate is lower than the organization’s raw checking capacity, because part of that capacity is spent re-proving what was already proven elsewhere. A portable, standardized evidence format — the kind SLSA-style attestations already provide for build provenance [ 15 ] — would let a downstream gate trust upstream verification instead of repeating it, raising effective $\mu$ without adding a single reviewer. Standardizing trust, in this framing, is not a separate story from verification capacity; it is one of the few available ways to increase $\mu$ without linearly increasing headcount or compute spent on checking.