arXiv: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
The proliferation of advanced LLMs has created a demand for deploying sophisticated AI capabilities closer to the data source in pervasive computing environments, necessitating new architectural approaches for embedded systems.
This development addresses a critical bottleneck in expanding agentic AI beyond server-class environments, potentially enabling widespread autonomy in devices with strict hardware constraints.
The proposed modular architecture provides a blueprint for integrating complex AI reasoning and tool use into embedded microcontrollers, moving AI agent capabilities from cloud/edge servers directly to the device level.
- · Embedded AI developers
- · IoT device manufacturers
- · Edge computing infrastructure
- · AI agents
- · Legacy embedded systems architecture
- · Cloud-dependent AI service providers for specific tasks
Wider deployment of autonomous AI agents in resource-constrained physical devices becomes feasible.
Increased demand for specialized embedded AI accelerators and optimized system-on-chip designs.
The blurring of lines between real-time control systems and cognitive AI, leading to more intelligent and adaptive physical interfaces and environments.
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Read at arXiv cs.AI