Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
Quick Take
This paper proposes a modular architecture for Embedded AI Agent Systems, addressing the challenges of deploying Large Language Models in resource-constrained environments. It introduces a tiered design separating On-Device Agents for low-latency tasks from Cloud-Augmented Agents for advanced reasoning, complemented by a Governance Layer for safety and policy enforcement.
Key Points
- Proposes a modular reference architecture for Embedded Agent Systems.
- Decouples On-Device Agents from Cloud-Augmented Agents for better performance.
- Integrates a Governance Layer for safety and policy enforcement.
- Addresses memory and energy constraints of embedded microcontrollers.
- Analyzes architectural trade-offs in latency, energy, and execution reliability.
Article Content
From source RSS / original summaryarXiv:2606. 02862v1 Announce Type: new Abstract: 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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