A Survey on Agent Workflow -- Status and Future
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909718598909952 |
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| author | Yu, Chaojia Cheng, Zihan Cui, Hanwen Gao, Yishuo Luo, Zexu Wang, Yijin Zheng, Hangbin Zhao, Yong |
| author_facet | Yu, Chaojia Cheng, Zihan Cui, Hanwen Gao, Yishuo Luo, Zexu Wang, Yijin Zheng, Hangbin Zhao, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Agent Workflow -- Status and Future Yu, Chaojia Cheng, Zihan Cui, Hanwen Gao, Yishuo Luo, Zexu Wang, Yijin Zheng, Hangbin Zhao, Yong Artificial Intelligence Human-Computer Interaction 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. |
| title | A Survey on Agent Workflow -- Status and Future |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.01186 |