An agent fleet scales until the work outgrows its context window. Then it churns: it re-derives what it already built and confidently undoes yesterday's fix.

ConceptBig idea 1 · the problem

Churn is the scaling limit

The MAGE method: the two theses that hold off churn and earn trustworthy software A cheap agent fleet is one input with two fates. Left ungoverned, it drifts into churn as its work outgrows the context window: drift, confidently-wrong. Run through the governed environment — which holds the Modeling Thesis (a typed model the fleet reasons through) and the Alignment Thesis (a mechanism that keeps output aligned with intent) — it converges on software that is high quality (correct), auditable (every change traces back to the models, so a result can be explained and reversed), and delivered at velocity. Model-Based Agentic Software Engineering is the governed path. Churn work outgrows the window; drift, confidently-wrong left ungoverned The agent fleet fast · cheap one input, two fates The governed environment Modeling Thesis a typed model the fleet reasons through Alignment Thesis keeps output aligned with intent Trustworthy software high quality · auditable at velocity traces to the models MAGE is the governed path: the two theses hold off churn.

Engineering intuition

A team of people slows under Brooks's Law; an agent fleet collapses when the work exceeds its window. Churn is the symptom; its causes are three not-knowings — what to build, how to realize it, how to change safely — and the theses below divide them: models treat the first two, governance the third.

Related concepts

Mechanisms

No mechanism edge declared yet — this concept ships thin (the edge is enriched in a later pass).

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