Loop Persistence
Loop Persistence (LP)
Loop Persistence is the measure of how consistently and autonomously AI agents execute work over time without requiring continuous human initiation, expressed as a coefficient from 0 to 1.
LP captures both the recurrence of repeated workflows and the continuity of workflows that operate persistently. It distinguishes a one-time task from a self-sustaining system that continues to produce work after the initiating human interaction ends.
A loop is any task or workflow that executes repeatedly or continuously — on a schedule, in response to events, or as a persistent background process. Persistence measures how long and how reliably that loop continues without manual restart or intervention.
Observable tiers
- Low LP — manual, one-off interactions initiated by users on demand. LP approaches 0.
- Moderate LP — scheduled or event-triggered workflows with periodic human oversight.
- High LP — continuously operating autonomous systems that monitor, decide, and act with limited intervention. LP approaches 1.
Where Agent Density determines how many agents work and Cognition Intensity determines how deeply each works, Loop Persistence determines how long the system keeps working. It converts token consumption from episodic usage into recurring demand and transforms AI from a responsive tool into a durable economic asset.
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