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.

翻译：如今，智能体人工智能正成为下一代通信网络的变革性范式，有望将大型语言模型从被动的聊天机器人演变为自主的操作者。然而，释放这一潜力需要弥合抽象推理与物理执行之间的关键鸿沟，这种能力我们称之为工具智能。本文探讨了赋能通信领域智能体人工智能的工具工程全景。我们首先分析了工具智能的功能及其对通信的影响。随后，我们提出了一个关于工具工程的系统性综述，涵盖从工具创建与发现、到选择、学习及基准测试的完整生命周期。此外，我们通过一个工具辅助无人机轨迹规划的案例研究，展示了工具智能在通信中的实现。通过引入一种带有可行性防护的教师引导强化学习方法，我们使智能体能够智能地操作工具。它们利用外部工具消除导航不确定性，同时在严格的能量约束下掌握成本感知的调度能力。本文旨在为构建6G时代的工具增强型智能体提供路线图。