What does this equation mean?

This is the specific problem a subagent is built to solve, and Claude Code’s own documentation gives a concrete, numeric illustration of the effect rather than an abstract claim about it. In a worked session example, a subagent delegated to research a piece of session-timeout handling reads three files totalling 6,100 tokens of raw source, entirely inside its own isolated window — “None of this touches your main context,” the documentation notes of the reads — and returns a single 420-token summary to the parent conversation, which the documentation flags directly: “That’s the context savings” [ 2 ] . The parent’s window absorbs an addition roughly a fourteenth the size of what was actually read. Put as a simple accounting identity, isolation does not shrink the total amount of computation a system performs; it relocates where that computation’s output lands: $T_{\mathrm{total}} = T_{\mathrm{parent}} + \sum_{i=1}^{k} T_{\mathrm{subagent}_i}, \qquad T_{\mathrm{parent}} \ll \sum_{i=1}^{k} T_{\mathrm{subagent}_i}$. The parent conversation’s occupied share of its own window, $T_{\mathrm{parent}}$ , stays small and bounded even as the total work performed across k delegated subagents grows without bound. That inequality is the real content behind the phrase “context savings” — the saving is measured in the one window that has to keep attending to everything else in the task, not in total tokens billed.