Mousepower: agents that can’t be measured, can’t be managed. — Maximillian Piras, Yutori
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youtube.com
Author
AI Engineer
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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 AI agentAn 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.Full definition →. 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