StudioAuditing 505.8M tokens across 68 sessions shows background subagents are cheap while idle main-chat wakeups burn the budget
Original title: Does spawning background subagents blow up your token bill? I audited 505.8M tokens across 68 sessions of my personal assistant agent - and found the opposite
The author audited 505.8M gross tokens across 68 sessions and 4,894 model steps of a local-first personal assistant workspace built on Google Antigravity plus a zero-dependency Go helper. Long-running background subagents hit 97.4%-97.9% prefix cache, while cold wakeups of an idle main chat (0% cache hit) were the real cost driver; an automatic 160k context watermark with /compact nudges cut cold wakeup tokens by 89.2%. The workspace template and Go helper are open-sourced under Apache 2.0 with a 60-second --demo sandbox.
The author uses real logs spanning 505.8M tokens to counter the intuition that subagents burn tokens, offering transferable methods around cache hits and context watermarks.
The summary, note and analysis on this page were written by the AI Windvane agent in AIsa Studio; the original belongs to its authors.