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I had my agent read its own usage bill, set five rules, and cut its token use by about a fifth

He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.

How it works

  1. Export the detailed usage file
  2. Hand it to the bot to study
  3. Find background subtasks and repeated context are the cost
  4. Set five standing rules in the bot memory
  5. Run a day
  6. Export again and compare

Why it’s worth knowing

The agent studied its own bill, found that its background subtasks were the problem, and the fix was measured on a second export the next day.

Where it’s from

Summarized from a public post on reddit.com. See the original ↗

Upgraded prompt

A ready-to-use prompt for this idea, built from research on what makes AI agents do the job right: who the AI should be, the exact steps, and a check to make sure it was done the way you wanted.

1Who the AI should be
2The prompt, with the steps
3The check when it’s done
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