Chat-based natural language interfaces have emerged as the dominant paradigm for human-agent interaction, yet they fundamentally constrain engagement with structured information and complex tasks. We identify three inherent limitations: the mismatch between structured data and linear text, the high entropy of unconstrained natural language input, and the lack of persistent, evolving interaction state. We introduce Software as Content (SaC), a paradigm in which dynamically generated agentic applications serve as the primary medium of human-agent interaction. Rather than communicating through sequential text exchange, this medium renders task-specific interfaces that present structured information and expose actionable affordances through which users iteratively guide agent behavior without relying solely on language. These interfaces persist and evolve across interaction cycles, transforming from transient responses into a shared, stateful interaction layer that progressively converges toward personalized, task-specific software. We formalize SaC through a human-agent-environment interaction model, derive design principles for generating and evolving agentic applications, and present a system architecture that operationalizes the paradigm. We evaluate across representative tasks of selection, exploration, and execution, demonstrating technical viability and expressive range, while identifying boundary conditions under which natural language remains preferable. By reframing interfaces as dynamically generated software artifacts, SaC opens a new design space for human-AI interaction, positioning dynamic software as a concrete and tractable research object.

翻译：基于聊天的自然语言界面已成为人机交互的主导范式，但其在处理结构化信息与复杂任务时存在根本性局限。我们识别出三个固有缺陷：结构化数据与线性文本之间的不匹配、非受限自然语言输入的高熵特性，以及缺乏持续演化的交互状态。本文提出"软件即内容"（SaC）范式，将动态生成的智能体应用程序作为人机交互的主要媒介。该媒介摒弃顺序文本交流方式，通过呈现任务专属界面来展示结构化信息，并提供可操作的功能接口，使用户无需完全依赖语言即可迭代式引导智能体行为。这些界面在交互周期中持续存在并演化，从瞬时响应转变为共享的有状态交互层，逐步收敛至个性化任务专属软件。我们通过人-智能体-环境交互模型对SaC进行形式化描述，推导出生成与演化智能体应用的设计原则，并提出实现该范式的系统架构。在选取、探索与执行三类典型任务上的评估验证了技术可行性和表达范围，同时识别出自然语言仍具优势的边界条件。通过将界面重新定义为动态生成的软件制品，SaC为人工智能交互开辟了全新设计空间，将动态软件确立为具体且可操作的研究对象。