让AI任务流程自动调整，提升复杂任务执行效率。
Flow: Modularized Agentic Workflow Automation
- 用图结构动态管理任务，根据历史表现实时优化子任务分配。
- 实测在多类任务中显著提升执行效率与容错能力。
- 适合需要灵活应变的自动化系统开发者或研究者。
基于大语言模型的多智能体框架在自动化规划与任务执行方面已取得显著成果。然而，执行过程中对智能体工作流的有效调整尚未得到充分研究。在真实场景中，初始计划必须实时应对突发挑战和环境变化，以确保复杂任务高效完成。本文将工作流定义为活动顶点图（AOV），允许通过大语言模型智能体基于历史表现和先前的AOV，动态调整子任务分配，实现持续的工作流优化。为提升框架性能，我们强调工作流设计的模块化，评估并平衡并行度与依赖复杂性。该设计使多智能体框架实现了子任务的高效并发执行、目标达成率提升及更强的错误容忍能力。在多种实际任务上的实验结果表明，通过动态工作流优化和模块化设计，多智能体框架的效率显著提高。代码已公开：https://github.com/tmllab/2025_ICLR_FLOW。
原文摘要 · Abstract (English)
Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of agentic workflows during execution has not been well studied. An effective workflow adjustment is crucial in real-world scenarios, as the initial plan must adjust to unforeseen challenges and changing conditions in real time to ensure the efficient execution of complex tasks. In this paper, we define workflows as an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by LLM agents through dynamic subtask allocation adjustment based on historical performance and previous AOVs. To further enhance framework performance, we emphasize modularity in workflow design based on evaluating parallelism and dependency complexity. With this design, our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance. Empirical results across various practical tasks demonstrate significant improvements in the efficiency of multi-agent frameworks through dynamic workflow refinement and modularization. The code is available at: https://github.com/tmllab/2025_ICLR_FLOW.
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