Flow: A Modular Approach to Automated Agentic Workflow Generation

2025-01-14

AI Summary

To address the weak dynamic workflow adaptability and poor fault tolerance of multi-agent systems in complex tasks, this paper proposes an evolvable workflow modeling and runtime re-planning framework based on Activity-on-Vertex (AOV) graphs. We pioneer the representation of workflows as dynamic AOV graphs, integrating dependency analysis with parallelism quantification to assess task dependency complexity and enable modular decoupling. Coupled with large language model (LLM)-based agents, the framework supports real-time rescheduling and autonomous error recovery guided by historical execution performance. Experimental results demonstrate significant improvements: +32.7% in task execution efficiency, +28.4% in goal achievement rate, and enhanced fault tolerance—enabling adaptive adjustment of highly concurrent subtasks under dynamic conditions.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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. A effective workflow adjustment is crucial, as in many real-world scenarios, 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) graphs. We continuously refine the workflow by dynamically adjusting task allocations based on historical performance and previous AOV with LLM agents. To further enhance system performance, we emphasize modularity in workflow design based on measuring parallelism and dependence complexity. Our proposed multi-agent framework achieved efficient sub-task concurrent execution, goal achievement, and error tolerance. Empirical results across different practical tasks demonstrate dramatic improvements in the efficiency of multi-agent frameworks through dynamic workflow updating and modularization.

Problem

• Multi-Agent Systems
• Dynamic Adjustment
• Task Execution Efficiency

Innovation

• Modular Tasking
• Dynamic Adjustment
• Multi-Agent Systems