Modern artificial intelligence (AI) systems increasingly operate in dynamic, resource-constrained environments where problem characteristics, data availability, and computational budgets evolve over time. Traditional AI models often assume fixed problem formulations and static resource allocations, limiting their adaptability and efficiency. This paper proposes a computation-theory–guided adaptive AI framework that explicitly integrates principles from computability theory, computational complexity, and resource-bounded computation to guide intelligent decision-making. The framework dynamically evaluates problem solvability, selects appropriate algorithmic strategies, and optimizes resource usage (time, memory, and energy) in real time. By mapping AI tasks to formal computational classes and leveraging adaptive control mechanisms, the proposed approach enhances robustness, scalability, and efficiency across diverse application domains. Conceptual analysis and illustrative use cases demonstrate the effectiveness of the framework in balancing solution quality with computational constraints.