为嵌入式设备设计可扩展的智能代理架构，兼顾低延迟与隐私安全。
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
- 分层设计：本地代理用压缩模型处理实时任务，云端代理用小语言模型进行规划。
- 引入治理层统一管理多设备安全与策略，支持分布式部署。
- 适合资源受限场景，如物联网设备、边缘智能系统开发者参考。
大型语言模型（LLMs）推动了具备复杂推理与工具调用能力的智能体发展；然而，在内存和能耗严格受限的嵌入式微控制器上实现此类自主性仍具挑战。现有框架多依赖服务器级算力或持续联网，难以适配深度嵌入式系统。本文提出一种模块化嵌入式智能体系统参考架构，弥合确定性实时控制与智能体智能之间的鸿沟。采用分层设计，将本地执行的压缩神经网络与规则逻辑的设备代理，与依赖小型语言模型（SLMs）进行高层推理与规划的云增强代理解耦。核心贡献是集成跨领域的治理层，保障分布式设备群的可观测性、策略执行与安全性。本文不依赖纯实验基准，而是分析资源受限环境下延迟、能耗与可靠执行间的架构设计原则与权衡。
原文摘要 · Abstract (English)
The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existing frameworks typically assume server-class resources or continuous connectivity, leaving a gap for deeply embedded systems. This paper proposes a modular reference architecture for Embedded Agent Systems that bridges the divide between deterministic real-time control and agentic intelligence. We introduce a tiered design that decouples On-Device Agents - executing highly compressed neural networks and rule-based logic for low-latency, privacy-critical tasks - from Cloud-Augmented Agents that leverage Small Language Models (SLMs) for higher-level reasoning and planning. A key contribution is the integration of a cross-cutting Governance Layer, ensuring observability, policy enforcement, and safety across distributed fleets of autonomous devices. Rather than presenting purely empirical benchmarks, we analyze architectural design principles and trade-offs regarding latency, energy, and reliable execution in resource-constrained environments.
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