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

Source: [AI Engineer](https://www.youtube.com/watch?v=8KkibGU_DDY)  
Feed7 permalink: https://feed7.dev/p/mousepower-agents-that-can-t-be-measured-can-t-be-managed-maximillian-pi-0q5vhzc  
Published: 2026-09-10T15:00:39.000Z  
Trust: Source Linked (source_linked)

## Why Included

Token counts do not show whether an agent was worth running. Define outcomes and cheap acceptance checks first, then reserve agents for work that is uncertain to execute but relatively easy to verify.

## Source Summary

The talk proposes “mousepower” as a mental model, not a literal metric: agent spend should connect tokens to outcomes such as bugs fixed or support requests closed, then to project objectives. Raw usage measures cost but not value.

## Practical Implication

Choose agent tasks using two questions: how uncertain is execution, and how uncertain is verification? Script low-uncertainty work, avoid tasks where checking requires repeating the work, and favor the **easier to verify than execute** middle ground.

## Agent-Ready Context

The talk proposes “mousepower” as a mental model, not a literal metric: agent spend should connect tokens to outcomes such as bugs fixed or support requests closed, then to project objectives. Raw usage measures cost but not value.

Choose agent tasks using two questions: how uncertain is execution, and how uncertain is verification? Script low-uncertainty work, avoid tasks where checking requires repeating the work, and favor the **easier to verify than execute** middle ground.

No universal ROI formula or production framework is supplied. The proposed **two-axis rubric** is a thought starter, and every team still needs domain-specific acceptance criteria that customers understand and trust.

## Connected Context

Feed7 judgment across 757 accumulated Signals:

“Mousepower” shifts agent evaluation from usage and output volume toward accepted outcomes tied to project value. Its two-axis rubric also narrows good automation targets to work that is uncertain enough to need an agent but materially easier to verify than execute. This complements architecture-aware and trace-based evals with a task-selection lens, while leaving each team to define trustworthy domain-specific acceptance criteria and ROI.

- [Designing Agents (The Floor Is the Frontier) — Ben Hylak, Raindrop](https://feed7.dev/p/designing-agents-the-floor-is-the-frontier-ben-hylak-raindrop-0uoems4) — Production failures, onset, and affected-user scope provide concrete outcome signals for the value-oriented measurement hierarchy proposed here.
- [From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI](https://feed7.dev/p/from-agent-traces-to-agent-simulations-rustem-feyzkhanov-snorkel-ai-0zwlzjq) — Replayable trace environments operationalize the rubric by comparing task success alongside cost, latency, and retries under fixed conditions.
- [Guide, Verify, Solve — Anirban Chatterjee, Sonar](https://feed7.dev/p/guide-verify-solve-anirban-chatterjee-sonar-1igfmbm) — Persistent quality warnings despite higher output reinforce the claim that throughput or token usage cannot stand in for verified project value.
- [Trading Desks to Clinical Trials: Parallels in Applied Vertical AI — Ayush Bhardwaj, Allos AI](https://feed7.dev/p/trading-desks-to-clinical-trials-parallels-in-applied-vertical-ai-ayush-1hwvsg3) — The need for domain-specific acceptance criteria aligns with this candidate’s requirement for narrow jobs and expert judgment where generic self-grading cannot establish usefulness.

## Context Map

- Layer: benchmark
- Domains: coding
- Topics: agent-evals, agent-reliability, harness-engineering

## Uncertainty

- No universal ROI formula or production framework is supplied. The proposed **two-axis rubric** is a thought starter, and every team still needs domain-specific acceptance criteria that customers understand and trust.

## Agent Instruction

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