Pattern, Persist! Algorithmic Information Conservation and the Thermodynamics of Agency

The word agent now names systems as different as a tool-using language model and a chemotactic cell, with no shared definition across the fields that use it. We argue that these uses converge on one substrate-independent structure—the algorithmic agent: model-mediated regulation built from an implicit or explicit Modeling Engine, a scalar Objective Function, and a Planning Engine. Its root is algorithmic persistence: a pattern whose compressed identity survives the filter of time. Holding a pattern bounded under perturbation takes a load-bearing regulator somewhere in the pattern–world system—a structure that absorbs, cancels, or exports what interaction would otherwise let accumulate. By the Algorithmic Regulator Theorem, this regulator shares mutual algorithmic information with the world: it carries a model of what it regulates and can be read as-if acting through an objective and a planner. The pattern is an agent only when this regulator is localized within it (self-regulation), and telehomeostatic only when the regulator’s objective is the pattern’s own persistence. Regulation in macroscopic systems acts on coarse-grained, many-to-one variables. It is therefore irreversible and exacts a Landauer cost — a price that scales with the coarse-graining and vanishes for reversible, equilibrium persistence (e.g., an isolated atom). Hence the thesis: a macroscopic agent is a persistent pattern that conserves its own bounded code through a thin, thermodynamically costly boundary, in a world that, when closed and reversible, conserves algorithmic information up to the fixed description of its law and time index. We use it to reframe the free-energy principle, evolution, and alignment: in a collective the parts need not share an objective, so alignment is an objective-distribution problem—the design of local objectives and of the constraints that bound them so that the whole persists, not the search for one correct reward.