Adaptive Genetic Repair Systems (AGRS): A Systems Architecture for AI-Assisted Biological Maintenance and Long-Horizon Longevity Research

Biological aging is increasingly understood as a systems-level phenomenon characterized by cumulative damage, declining repair fidelity, and degraded feedback regulation across cellular and molecular processes. While advances in genetics, molecular biology, and longevity science have yielded promising localized interventions, the absence of a unifying, adaptive coordination framework remains a limiting factor.This paper introduces Adaptive Genetic Repair Systems (AGRS) as a conceptual systems architecture for integrating artificial intelligence, adaptive computing, and biological monitoring into a cohesive framework for biological maintenance and resilience research. AGRS does not propose direct genetic modification by artificial intelligence. Instead, it defines an advisory and orchestration layer that models damage accumulation, prioritizes repair pathways, and supports human-in-the-loop decision-making across heterogeneous biological subsystems.Architectural constraints, safety boundaries, ethical considerations, and governance requirements are examined, with emphasis on transparency, override mechanisms, and non-weaponizable design. The purpose of this work is not to assert near-term feasibility or clinical outcomes, but to provide a structured foundation upon which interdisciplinary research collaborations may evaluate and refine adaptive approaches to long-horizon biological maintenance.