梳理智能体工作流系统现状，揭示其架构与能力演进路径。
A Survey on Agent Workflow -- Status and Future
- 按功能与架构双维度分类20余种智能体工作流系统
- 发现规划、多智能体协作与API集成是核心能力方向
- 适合关注AI自动化与安全可控的开发者与研究者
在大语言模型时代，自主智能体已成为实现通用智能的强大范式。这些智能体通过动态调用工具、记忆和推理能力来完成用户定义的目标。随着智能体系统日益复杂，结构化的工作流——即编排框架——成为实现可扩展、可控制和安全AI行为的核心。本综述全面回顾了智能体工作流系统，涵盖学术框架与工业实现。我们从功能能力（如规划、多智能体协作、外部API集成）与架构特征（如智能体角色、编排流程、描述语言）两个维度对现有系统进行分类。通过对20余个代表性系统的比较，我们提炼出共性模式、潜在技术挑战与新兴趋势。进一步探讨了工作流优化策略与安全问题。最后，指出标准化与多模态融合等开放问题，为智能体设计、工作流基础设施与安全自动化交叉领域的未来研究提供洞见。
原文摘要 · Abstract (English)
In the age of large language models (LLMs), autonomous agents have emerged as a powerful paradigm for achieving general intelligence. These agents dynamically leverage tools, memory, and reasoning capabilities to accomplish user-defined goals. As agent systems grow in complexity, agent workflows-structured orchestration frameworks-have become central to enabling scalable, controllable, and secure AI behaviors. This survey provides a comprehensive review of agent workflow systems, spanning academic frameworks and industrial implementations. We classify existing systems along two key dimensions: functional capabilities (e.g., planning, multi-agent collaboration, external API integration) and architectural features (e.g., agent roles, orchestration flows, specification languages). By comparing over 20 representative systems, we highlight common patterns, potential technical challenges, and emerging trends. We further address concerns related to workflow optimization strategies and security. Finally, we outline open problems such as standardization and multimodal integration, offering insights for future research at the intersection of agent design, workflow infrastructure, and safe automation.
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