Agent Ontogeny & Lineage Physics (AOLP): A Developmental Law Framework for Artificial Intelligence

Current artificial intelligence research is dominated by the paradigms of optimization, learning, and evolution. While these frameworks have yielded high-performance task-specific agents, they operate under the implicit assumption that the space of agent development is unconstrained intelligence is viewed as a fluid commodity that emerges proportionally to data volume, compute, or reward density. This paper argues that such an approach ignores a fundamental scientific dimension: the structural laws that govern how an agent is allowed to become intelligent over time. We introduce Agent Ontogeny & Lineage Physics (AOLP), a foundational framework that reframes artificial development as a constrained physical process governed by deterministic laws. By shifting the focus from "what an agent learns" to "the lawful constraints under which it develops," we define the Developmental Trajectory Constraint System (DTCS) a new scientific object that enforces irreversibility and phase-dependent growth. We formalize the primitives of Developmental Laws (DLs), Ontogenetic Phases, and Lineage-level constraints. Unlike traditional regularization or curriculum learning, AOLP treats these constraints as "physics" structural boundaries that, when violated, result in irreversible developmental regression rather than transient performance penalties. This paradigm provides the mathematical and conceptual tools to analyse Developmental Collapse and Counterfactual Recovery Horizons, offering a new axis for understanding AI safety, stability, and the transition toward robust, self-modelling artificial intelligences.