Large language models and related generative AI systems increasingly operate in safety-critical and high-impact settings, where reliability, alignment, and robustness under distribution shift are central concerns. While retrieval-augmented generation (RAG) has emerged as a practical mechanism for grounding model outputs in external knowledge, it does not by itself provide guarantees against system-level failure modes such as hallucination, mis-grounding, or deceptively stable unsafe behavior. This work introduces SORT-AI, a structural safety and reliability framework that models advanced AI systems as chains of operators acting on representational states under global consistency constraints. Rather than proposing new architectures or empirical benchmarks, SORT-AI provides a theoretical and diagnostic perspective for analyzing alignment-relevant failure modes, structural misgeneralization, and stability breakdowns that arise from the interaction of retrieval, augmentation, and generation components. Retrieval-augmented generation is treated as a representative and practically relevant testbed, not as the primary contribution. By analyzing RAG systems through operator geometry, non-local coupling kernels, and global projection operators, the framework exposes failure modes that persist across dense retrieval, long-context prompting, graph-constrained retrieval, and agentic interaction loops. The resulting diagnostics are architecture-agnostic and remain meaningful across datasets, implementations, and deployment contexts. SORT-AI connects reliability assessment, explainability, and AI safety by shifting evaluation from local token-level behavior to global structural properties such as fixed points, drift trajectories, and deceptive stability. While illustrated using RAG, the framework generalizes to embodied agents and quantum-inspired operator systems, offering a unifying foundation for safety-oriented analysis of advanced AI systems.