Cleanest draft of the series. The "loop" definition is well-handled, the examples are concrete, and the closing line is consistent with the framework's through-line. Minor issues only:
Fact-check flags:
"A loop refers to a task or workflow that repeats over time" — accurate but slightly narrow. Some high-LP workflows are not strictly repeating — they are continuous rather than cyclical (e.g., a monitoring agent that runs permanently rather than triggering on a schedule). The definition should cover both recurrence and continuity to avoid a technical objection.
"It represents the durability of work" — "durability" is an interesting word choice but it's doing double duty with "persistence" and slightly muddies the definition. The cleaner concept is autonomy sustained over time — that's what LP is actually measuring.
Structural observations:
The examples section is the strongest part of the entry and it's underweighted. Three bullet examples are good but they all cluster around the same type (monitoring/optimization/chaining). A lexicon entry benefits from one example that shows low LP and one that shows the transition from low to high — it makes the coefficient feel measurable rather than abstract.
The entry is missing the LP coefficient definition (0 to 1) that appears in TCM, CT, and A-ARPU entries. Needs to be here for consistency.
The closing line is strong but it's the same closing line as CT and A-ARPU. By the third time a lexicon reader sees "transitions from tools that assist productivity to infrastructure that independently generates output," it loses force. This entry needs its own closing beat — one specific to what LP contributes that AD and CI don't.
Final version:
The measure of how consistently and autonomously AI agents execute work over time without requiring continuous human input, expressed as a coefficient from 0 to 1. LP captures both the recurrence of repeated workflows and the continuity of workflows that run permanently — distinguishing between one-time task execution and self-sustaining system-driven work.
LP is the third primary driver of Cognition Throughput and the variable most directly responsible for transforming token consumption from episodic usage into recurring, predictable demand.
In this model:
A loop refers to any task or workflow that executes repeatedly or continuously over time — whether on a schedule, triggered by events, or operating as a persistent background process.
Persistence measures how long and how reliably that loop continues without human initiation or interruption. A fully human-initiated, one-off interaction has LP near 0. A fully autonomous, continuously operating workflow approaches LP of 1.
LP produces three observable tiers:
Low LP — manual, one-off interactions initiated by users on demand. Token consumption is episodic and unpredictable. Revenue profile resembles traditional software usage.
Moderate LP — scheduled or semi-automated workflows that recur on defined intervals. Human oversight is still present but initiation is delegated to the system.
High LP — fully autonomous systems operating continuously in the background without human intervention. Examples include agents monitoring live data streams and triggering downstream actions in real time, multi-agent systems executing chained workflows end-to-end, and continuous process optimization loops that self-adjust based on observed outcomes.
Where Agent Density determines how many workers are deployed and Cognition Intensity determines how hard each works, Loop Persistence determines how long the system keeps working. It is the variable that converts AI from a responsive tool into a durable economic asset — one that generates output whether or not a human is actively engaged.
See also: Agent Density, Cognition Intensity, Cognition Throughput, Tokenized Cognition Model, Digital Labor Economics
Read Signal Briefs: The emergence of digital labor economics
Read Frameworks: Agent Experience Integrity (AXI)
Read Lexicon: Digital Labor Economics (DLE)
Read Lexicon: Cognition Throughput
Read Lexicon: Cognition Intensity