{
  "annotation_id": "c1ddb790-318e-4cf1-bdee-4b2d9e2bfa0f",
  "date_created": "2026-02-17T05:53:15.777000Z",
  "date_modified": "2026-02-17T05:53:15.777000Z",
  "file_hash": "99c2c7ab9ac888c2d8295ccab70ecd8e5e4adc643354d59c129059c75e77ecee",
  "private": false,
  "record": {
    "abstract": "Nowadays, agentic AI is emerging as a transformative paradigm for next-generation communication networks, promising to evolve large language models (LLMs) from passive chatbots into autonomous operators. However, unleashing this potential requires bridging the critical gap between abstract reasoning and physical actuation, a capability we term tool intelligence. In this article, we explore the landscape of tool engineering to empower agentic AI in communications. We first analyze the functionalities of tool intelligence and its effects on communications. We then propose a systematic review for tool engineering, covering the entire lifecycle from tool creation and discovery to selection, learning, and benchmarking. Furthermore, we present a case study on tool-assisted uncrewed aerial vehicles (UAV) trajectory planning to demonstrate the realization of tool intelligence in communications. By introducing a teacher-guided reinforcement learning approach with a feasibility shield, we enable agents to intelligently operate tools. They utilize external tools to eliminate navigational uncertainty while mastering cost-aware scheduling under strict energy constraints. This article aims to provide a roadmap for building the tool-augmented intelligent agents of the 6G era.",
    "arxiv_id": "2601.08259",
    "authors": [
      "Yinqiu Liu",
      "Ruichen Zhang",
      "Dusit Niyato",
      "Abbas Jamalipour",
      "Trung Q. Duong",
      "Dong In Kim"
    ],
    "categories": [
      "cs.NI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Unleashing Tool Engineering and Intelligence for Agentic AI in Next-Generation Communication Networks",
    "url": "https://arxiv.org/abs/2601.08259",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e80ffedc-4428-42fd-8a12-0eb1b8f5c6cc",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}