Audit where your agent team's tokens actually go

What you end up with

His estimate is that starting sub-tasks with fresh workers and a tight brief, and returning only the answer, could have cut usage roughly in half.

What you get

• A plain-language summary of the workflow
• A fill-in prompt template
• What to watch out for
• A link to the original source

Who it is for

Builders who already use AI agents and want a proven workflow they can copy into their own project.

Works with

Dashed chip: Plain Markdown, so it also pastes into other agents such as Grok Bot, Hermes, OpenClaw or Muse. We list those as compatible by format only, not as tested.

Tools needed

Cost to run: Free with the agent plan you already use.
Time to result: One working session

How it works

1. Export your usage and sort runs by total tokens; check how few runs ma

   Export your usage and sort runs by total tokens; check how few runs make up half the total.

   Expected: Done before you move to the next step.

2. Split each step into cache reads, cache writes, output and fresh input

   Split each step into cache reads, cache writes, output and fresh input.

   Expected: Done before you move to the next step.

3. Find the handoff points between agents and measure tokens per handoff

   Find the handoff points between agents and measure tokens per handoff.

   Expected: Done before you move to the next step.

4. Look for long-lived workers that get resumed for unrelated tasks

   Look for long-lived workers that get resumed for unrelated tasks.

   Expected: Done before you move to the next step.

5. Try fresh workers with a short brief that return only the result, and

   Try fresh workers with a short brief that return only the result, and compare.

   Expected: Done before you move to the next step.

Sample prompt from the pack

Apply this workflow to [your project]:
1. Export your usage and sort runs by total tokens; check how few runs make up half the total.
2. Split each step into cache reads, cache writes, output and fresh input.
3. Find the handoff points between agents and measure tokens per handoff.
4. Look for long-lived workers that get resumed for unrelated tasks.
5. Try fresh workers with a short brief that return only the result, and compare.
Stop and ask me before anything irreversible: deploys, payments, sends or deletes.

Free preview of the file

# Audit where your agent team's tokens actually go

PaperChase agent use case (free feed entry), version 1.0. Source: [u/AdithyaBandara on r/cursor](https://www.reddit.com/r/cursor/comments/1xaki84/i_traced_where_my_grok_bot_tokens_were_going_the/), 2026-10-20.

## What they did

About 75% of 75.35 million tokens went to background worker runs, and just 5 of roughly 100 runs made up half the total. One hour where a coordinator handed jobs to teammate bots used 13.19 million tokens; each handoff and reply cost between about 495,000 and 3.65 million tokens, because the receiving bot re-read its whole history, often started a worker that re-read its own, and then the coordinator re-read everything again. One worker that kept being resumed task after task grew to about 303,000 tokens of context and burned 4.24 million tokens in that hour. By type, 93.8% were cache reads, 5.1% cache writes, about 1% output and almost no fresh input: the median step re-read about 78,000 tokens to write about 300.

## Worth knowing

His estimate is that starting sub-tasks with fresh workers and a tight brief, and returning only the answer, could have cut usage roughly in half. The numbers are partly reconstructed from timing and cache chains because the export does not say which bot did what, and he could not turn them into a dollar figure since plan weighting is not published. The pattern applies to any multi-agent setup, not only this one.

Worth knowing

• His estimate is that starting sub-tasks with fresh workers and a tight brief, and returning only the answer, could have cut usage roughly in half. The numbers are partly reconstructed from timing and cache chains because the export does not say which bot did what, and he could not turn them into a dollar figure since plan weighting is not published. The pattern applies to any multi-agent setup, not only this one.
• Figures are as reported by the original author and were not re-measured by us.

Details

Version 1.0 · Updated Oct 25, 2026 · Markdown version of this page
License: Personal and commercial use. Credit the original source when you share the workflow. Download terms

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