How can LLMs dynamically adjust multi-agent workflows?
Flow: A Modular Approach to Automated Agentic Workflow Generation
This paper introduces Flow, a multi-agent framework utilizing LLMs for improved automated task completion. It dynamically updates workflows represented as directed acyclic graphs (AOV) to adapt to changing conditions and emphasizes modular task design to enhance parallelism and reduce dependencies. This allows for flexible task allocation and agent role adjustments during runtime, leading to greater efficiency and robustness in completing complex tasks compared to existing multi-agent systems. Key to LLM-based multi-agent systems is the use of prompts to generate and refine workflows, along with dynamic updates based on task progress and error handling. The modularity of the workflows facilitates these dynamic adjustments, enhancing the system's ability to adapt to new challenges.