Mousepower: agents that can’t be measured, can’t be managed.

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Agents have a measurement problem, which makes them impossible to efficiently manage. You’ve likely

heard many say execution is now cheap, but judgement is the new bottleneck. This is because our

evaluation frameworks weren’t designed for systems that tirelessly output in parallel. The canary in

the coal mine is code generation becoming largely solved at the expense of breaking code review. As

agents reverberate across all knowledge work, the same fracture will spread to artifacts, actions, &

decisions. Yet without a scalable quality measure, we can’t ascend to a higher level of abstraction

because we won’t trust the foundation below. So how do we design measurements that are efficient,

intuitive, & trustworthy? Past paradigm shifts offer inspiration, such as James Watt not just

building a better engine but also inventing horsepower to map it onto existing mental models. We

need an equivalent quantification to communicate the “mousepower” of agents. Information theory

gives us the starting point: concepts like entropy, ergodic processes, and Hamiltonian problems

point us toward the most tractable trajectories — easier to verify than they are to solve.

Related YouTube Video

The Bitter Layout or: How I Learned to Love the Model Picker — Maximillian Piras, Yutori (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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