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Building & Agents · 1 min de lectura

When Humans Hold the Loop: Mochary for Agent-First Teams

The operating principles in Matt Mochary's Mochary Method were designed for human organizations, but they translate almost perfectly to the accountability gaps that appear when agents start doing the work. The translation is simple: every place Mochary inserts a human check, an AI-native founder must now decide whether that check belongs to a human or to an eval.

Con · estudiado y reformulado para builders AI-native“The Mochary Method” — Matt Mochary

Written Decisions Are Your Evals in Disguise

Mochary insists that decisions be written down before they are debated, because writing forces precision. For founders running agentic workflows, this discipline is structural, not just cultural. A task handed to an agent without a written definition of done is a workflow without a success criterion — which means it has no eval, and an agent without an eval is untestable in production. The practice of writing the decision first is exactly the practice of writing the eval first, and both habits live or die together.

Fear and Anger as System Signals

Mochary treats fear and anger not as noise to suppress but as information about where a system is broken. In an agent-first company, the emotional equivalent is the moment a founder feels uneasy handing a task to an agent entirely. That unease is a signal, not a character flaw. It usually means the task lacks a clear scope, the tool surface is vague, or there is no harness to catch a bad output. Founders who override that signal with confidence are skipping the design work the feeling was pointing at.

Accountability Cadences Map onto Feedback Loops

Mochary's weekly check-ins and issue-resolution rhythms exist to keep humans from drifting out of alignment silently. Agents drift the same way: a workflow that ran cleanly in staging can degrade in production over weeks without a regular review cadence. The Mochary habit of naming the issue, assigning an owner, and closing the loop is the same discipline as scheduling eval reviews, watching for output distribution shift, and keeping a human accountable for the agent's performance — not just its deployment.

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