Generative large language models (hereinafter referred to as LLMs) have demonstrated outstanding capabilities in language understanding, content generation, and interactive problem-solving by modeling statistical regularities in massive corpora. However, the mechanisms underlying these capabilities also constrain their applicability and reliability. Specifically, LLMs’ capabilities are constrained by three types of mechanistic conditions: training data that determines knowledge coverage and temporal scope; the structural expressive boundaries of the Transformer architecture under finite conditions; and inherent hallucinations stemming from probabilistic generation objectives. These upper-bound constraints on model capability further manifest in practical applications as boundaries across task categories, the severity of potential consequences, and data forms: the misalignment between generative models and discriminative tasks; the low tolerance of safety-critical scenarios for black-box hallucinations; and the incompatibility between serialized representations and structured data. To address the limitations of monolithic models regarding knowledge, computation, verification, and accountability, the critical evolutionary path lies in transitioning toward model systems. Agentic workflows constitute an important form of model systems, overcoming the capability limits of monolithic models by integrating components such as external memory, retrieval, and tool calling. At the same time, these systems must establish anchors of trust through a closed loop of generation and symbolic verification, following the principle that “generation is handled by the model while verification is handled by the system.” Furthermore, a human-in-the-loop mechanism must be embedded to ensure the accountability of final decisions. The development of LLMs needs to shift from scaling up model parameters to constructing trustworthy systems, moving toward building explainable, verifiable, and accountable model systems, so that they truly become reliable tools that assist humans in solving problems.