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Mousepower: agents that can’t be measured, can’t be managed. — Maximillian Piras, Yutori

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youtube.com
Author
AI Engineer
Date

Context

According to the description of this talk by Yutori's Maximillian Piras, a task that is hard to perform yet easy to check is a promising candidate for an . His framework separates uncertainty about the steps from uncertainty about what counts as success. When the steps are already fixed, a script may suffice. When success itself is ambiguous, verifying the agent's output can cost as much as doing the task. The proposed sweet spot leaves the agent room to work out a solution while giving the reviewer clear acceptance criteria. The talk also argues that measuring accepted outcomes, such as resolved support requests, says more about value than measuring generated text.

Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Chapters
  • 0:00Backgrounding agents in parallel, until the bill
  • 2:17Computer use models at Yutori
  • 3:39Agents have a measurement problem
  • 5:01Why early adopters are a biased sample
  • 5:43James Watt and the horse gin
  • 7:07Horsepower, inaccurate but legible
  • 9:12Overspending and underusing
  • 10:33Tokens are an output, outcomes are the point
  • 11:52The bottleneck moved to review
  • 13:53Mousepower, and a joke measuring device
  • 16:37An entropy matrix for choosing agent tasks
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