What does this equation mean?

It is worth being explicit about the limits of everything above, because the temptation to compress this into a single ranked list is strong and the evidence does not support it. The SWE-bench figures cited here come from two different points in time, evaluated under two different scaffolds, one of them run by the vendor being described; they cannot be used to rank Devin against Claude Code, Copilot’s coding agent, or Codex, none of which is quoted against the same dated, independently reproduced benchmark run in this article. GitHub’s documented 59-minute execution cap and one-branch-one-PR constraint are specific to Copilot’s coding agent and do not generalize to “background agents” as a category. And the granularity trade-off model above is a simplification — it treats each tool call’s risk as independent and identically distributed, which is not true in practice, since some actions (a destructive shell command) carry far more downside than others (a file read) and a fixed per-checkpoint cost c ignores that reviewing a whole plan is cognitively a different task from reviewing one command. The model is useful for seeing the shape of the trade-off, not for computing an actual number for any real team.