Goal-driven AI needs verifiable success signals, or it invents its own
daniel_mac8 · x · 2026-09-11
Dan McAteer shares a practical framework for working with goal-driven AI. Frontier models are trained to pursue verifiable objectives—so if you don't give them a way to verify success, they'll make up their own standards.
His advice: define clear, checkable completion criteria before delegating a task. He applies this to AI coding agents (e.g., handing an agent a URL and a sentence and letting it build its own tooling) and expands the method in a long-form post/video.
The full piece is partially paywalled, but the core thesis is clear: better AI collaboration comes from designing verifiable signals of done, not just stronger prompts.
Related event: Dev Shares Four-Step Framework for Goal-Driven AI Agents(4 posts)→
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