系统梳理大模型产业智能体的技术、应用与评估，助力落地实践。
Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
- 构建能力成熟度框架，解析智能体从流程执行到自适应协作的演进路径。
- 揭示记忆、规划、工具使用三大技术支柱如何支撑复杂任务自主执行。
- 聚焦真实场景挑战，为产业智能体研发提供可落地的评估与治理思路。
随着大语言模型（LLMs）的发展，具备自主推理、规划和执行复杂任务能力的智能体已成为人工智能前沿。然而，如何将通用智能体研究转化为推动产业变革的生产力仍面临重大挑战。本文基于产业智能体能力成熟度框架，系统综述了基于LLM的产业智能体在技术、应用与评估方法上的进展。首先，分析支撑智能体能力发展的三大核心技术支柱：记忆、规划与工具使用，探讨其从支持简单任务向赋能复杂自主系统及集体智能的演化过程。其次，概述智能体在数字工程、科学发现、具身智能、协同业务执行和复杂系统仿真等真实领域中的应用。此外，回顾基础与专用能力的评估基准与方法，指出当前评估体系在真实性、安全性和行业适配性方面存在的挑战。最后，深入探讨产业智能体在实际应用中面临的能力边界、发展潜力与治理问题，并提出未来发展方向。通过结合技术演进与产业实践，本综述旨在厘清现状，为下一代产业智能体的理解与构建提供清晰路线图与理论基础。
原文摘要 · Abstract (English)
With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on LLMs. Using an industry agent capability maturity framework, it outlines the evolution of agents in industry applications, from "process execution systems" to "adaptive social systems." First, we examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use. We discuss how these technologies evolve from supporting simple tasks in their early forms to enabling complex autonomous systems and collective intelligence in more advanced forms. Then, we provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation. Additionally, this paper reviews the evaluation benchmarks and methods for both fundamental and specialized capabilities, identifying the challenges existing evaluation systems face regarding authenticity, safety, and industry specificity. Finally, we focus on the practical challenges faced by industry agents, exploring their capability boundaries, developmental potential, and governance issues in various scenarios, while providing insights into future directions. By combining technological evolution with industry practices, this review aims to clarify the current state and offer a clear roadmap and theoretical foundation for understanding and building the next generation of industry agents.
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