{
  "id": 3264,
  "url": "https://arxiv.org/abs/2606.02862v1",
  "title": "Toward a Modular Architecture for Embedded AI Agent Systems at the Edge",
  "summary": "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 th",
  "authors": "Marcus Rüb, Michael Gerhards",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-01T20:24:18.000Z",
  "fetched_at": "2026-07-14T16:30:09.958Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3264",
  "original_url": "https://arxiv.org/abs/2606.02862v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}