With the deep integration of artificial intelligence and smart home technologies, the intelligent transformation of traditional household appliances has become an inevitable trend. This paper presents AirAgent--an LLM-driven autonomous agent framework designed for home air systems. Leveraging a voice-based dialogue interface, AirAgent autonomously and personally manages indoor air quality through comprehensive perception, reasoning, and control. The framework innovatively adopts a two-layer cooperative architecture: Memory-Based Tag Extraction and Reasoning-Driven Planning. First, a dynamic memory tag extraction module continuously updates personalized user profiles. Second, a reasoning-planning model integrates real-time environmental sensor data, user states, and domain-specific prior knowledge (e.g., public health guidelines) to generate context-aware decisions. To support both interpretability and execution, we design a semi-streaming output mechanism that uses special tokens to segment the model's output stream in real time, simultaneously producing human-readable Chain-of-Thought explanations and structured, device-executable control commands. The system handles planning across 25 distinct complex dimensions while satisfying more than 20 customized constraints. As a result, AirAgent endows home air systems with proactive perception, service, and orchestration capabilities, enabling seamless, precise, and personalized air management responsive to dynamic indoor and outdoor conditions. Experimental results demonstrate up to 94.9 percent accuracy and more than 20 percent improvement in user experience metrics compared to competing commercial solutions.

翻译：随着人工智能与智能家居技术的深度融合，传统家用电器的智能化转型已成为必然趋势。本文提出AirAgent——一个面向家庭空气系统的、由大语言模型驱动的自主智能体框架。该框架通过基于语音的对话接口，借助全面的感知、推理与控制能力，自主化、个性化地管理室内空气质量。该框架创新性地采用双层协同架构：基于记忆的标签提取与推理驱动的规划。首先，动态记忆标签提取模块持续更新个性化用户画像。其次，推理规划模型整合实时环境传感器数据、用户状态以及领域先验知识（例如公共卫生指南），生成情境感知的决策。为同时支持可解释性与可执行性，我们设计了一种半流式输出机制，利用特殊标记实时分割模型的输出流，同步生成人类可读的思维链解释和结构化、设备可执行的控制指令。该系统能够处理跨越25个不同复杂维度的规划任务，同时满足超过20项定制化约束。因此，AirAgent赋予家庭空气系统以主动感知、服务与编排能力，实现响应动态室内外环境的无缝、精准且个性化的空气管理。实验结果表明，与现有商业解决方案相比，本系统准确率最高可达94.9%，用户体验指标提升超过20%。