FlowForge: Guiding the Creation of Multi-Agent Workflows with Design Space Visualization as a Thinking Scaffold
Pan Hao, Dongyeop Kang, Nicholas Hinds, Qianwen Wang
摘要
Multi-agent workflows have become an effective strategy for tackling complicated tasks by decomposing them into multiple sub-tasks and assigning them to specialized agents. However, designing optimal workflows remains challenging due to the vast and intricate design space. Current practices rely heavily on the intuition and expertise of practitioners, often resulting in design fixation or an unstructured, time-consuming exploration of trial-and-error. To address these challenges, this work introduces FLOWFORGE, an interactive visualization tool to facilitate the creation of multi-agent workflow through i) a structured visual exploration of the design space and ii) in-situ guidance informed by established design patterns. Based on formative studies and literature review, FLOWFORGE organizes the workflow design process into three hierarchical levels (i.e., task planning, agent assignment, and agent optimization), ranging from abstract to concrete. This structured visual exploration enables users to seamlessly move from high-level planning to detailed design decisions and implementations, while comparing alternative solutions across multiple performance metrics. Additionally, drawing from established workflow design patterns, FLOWFORGE provides context-aware, in-situ suggestions at each level as users navigate the design space, enhancing the workflow creation process with practical guidance. Use cases and user studies demonstrate the usability and effectiveness of FLOWFORGE, while also yielding valuable insights into how practitioners explore design spaces and leverage guidance during workflow development.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper21
- Reflexion: language agents with verbal reinforcement learningNoah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan 等NeurIPS 2023 · 被引用 5,828 次
- Generative Agents: Interactive Simulacra of Human BehaviorJoon Sung Park, Joseph C. O'Brien, Carrie Jun Cai, Meredith Ringel Morris 等UIST 2023 · 被引用 1,882 次
- Improving Factuality and Reasoning in Language Models through Multiagent DebateYilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum 等ICML 2024 · 被引用 1,562 次
- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent DebateChi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu 等ICLR 2024 · 被引用 871 次
- AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model PromptsTongshuang Wu, Michael Terry, Carrie Jun CaiCHI 2022 · 被引用 465 次
相关 Paper
- Multi-Agent Design: Optimizing Agents with Better Prompts and TopologiesHan Zhou, Xingchen Wan, Ruoxi Sun, Hamid Palangi 等ICLR 2026 · 被引用 127 次
- FlowSearcher: Synthesizing Memory-Guided Agentic Workflows for Web Information SeekingKeyi Xiang, Zeyu Feng, Zhuoyi Lin, Yueming Lyu 等ICLR 2026
- Flow: Modularized Agentic Workflow AutomationBoye Niu, Yiliao Song, Kai Lian, Yifan Shen 等ICLR 2025
- CompositingVis: Exploring Interactions for Creating Composite Visualizations in Immersive EnvironmentsQian Zhu, Tao Lu, Shunan Guo, Xiaojuan Ma 等IEEE VIS 2024 · 被引用 11 次
- Virtual Minds, Real Work: LLM-Powered Preference-Based Planning through Spatial Multi-Agent-Human CollaborationZiyi Zhang, Xin Yi, Zitong Dai, Xuewen Yu 等CHI 2026 · 被引用 1 次
