让AI自动设计智能体工作流，无需人工定义操作符。
$A^2Flow:$ Automating Agentic Workflow Generation via Self-Adaptive Abstraction Operators
- 用自适应抽象操作符自动构建工作流，替代人工预设。
- 在通用和具身任务上性能提升19.3%，资源消耗降低37%。
- 适合研究AI自动化流程设计与智能体系统开发的读者。
大型语言模型在自动化智能体工作流设计方面展现出巨大潜力。然而，现有方法仍严重依赖人工预定义的操作符，限制了泛化性和可扩展性。为此，我们提出A²Flow，一个基于自适应抽象操作符的全自动智能体工作流生成框架。A²Flow采用三阶段操作符提取流程：1）基于案例的初始操作符生成：利用专家示范和LLM推理生成特定案例的操作符；2）操作符聚类与初步抽象：将跨任务相似操作符分组，形成初步抽象；3）深度抽象执行操作符提取：通过长链式思维提示和多路径推理，提炼出紧凑且可泛化的执行操作符。这些操作符作为可复用的构建模块，无需人工预定义即可用于工作流构造。此外，我们引入操作符记忆机制增强节点级工作流搜索，通过保留历史输出丰富上下文，提升决策能力。在通用和具身基准测试中，A²Flow相较当前最优基线平均性能分别提升2.4%和19.3%，资源使用减少37%。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown strong potential in automating the design of agentic workflows. However, existing methods still rely heavily on manually predefined operators, limiting generalization and scalability. To address this issue, we propose $A^2Flow$, a fully automated framework for agentic workflow generation based on self-adaptive abstraction operators. $A^2Flow$ employs a three-stage operator extraction process: 1) Case-based Initial Operator Generation: leveraging expert demonstrations and LLM reasoning to generate case-specific operators; 2) Operator Clustering and Preliminary Abstraction: grouping similar operators across tasks to form preliminary abstractions; and 3) Deep Extraction for Abstract Execution Operators: applying long chain-of-thought prompting and multi-path reasoning to derive compact and generalizable execution operators. These operators serve as reusable building blocks for workflow construction without manual predefinition. Furthermore, we enhance node-level workflow search with an operator memory mechanism, which retains historical outputs to enrich context and improve decision-making. Experiments on general and embodied benchmarks show that $A^2Flow$ achieves a 2.4\% and 19.3\% average performance improvement and reduces resource usage by 37\% over state-of-the-art baselines. Homepage:https://github.com/pandawei-ele/A2FLOW
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