F low B ench: Revisiting and Benchmarking Workflow-Guided Planning for LLM -based Agents (2024)

Abstract: LLM-based agents have emerged as promising tools, which are crafted to fulfill complex tasks by iterative planning and action. However, these agents are susceptible to undesired planning hallucinations when lacking specific knowledge for expertise-intensive tasks. To address this, preliminary attempts are made to enhance planning reliability by incorporating external workflow-related knowledge. Despite the promise, such infused knowledge is mostly disorganized and diverse in formats, lacking rigorous formalization and comprehensive comparisons. Motivated by this, we formalize different formats of workflow knowledge and present FlowBench, the first benchmark for workflow-guided planning. FlowBench covers 51 different scenarios from 6 domains, with knowledge presented in diverse formats. To assess different LLMs on FlowBench, we design a multi-tiered evaluation framework. We evaluate the efficacy of workflow knowledge across multiple formats, and the results indicate that current LLM agents need considerable improvements for satisfactory planning. We hope that our challenging benchmark can pave the way for future agent planning research.

## introduction the impressive advances of large language models (llms) @xcite @xcite have spurred the evolution of llm-driven agents @xcite for complex task solving across diverse domains @xcite @xcite . while recent works have made strides in advancing tool utilization abilities of agents through prompt construction @xcite figure 1 : the procedure of workflow-guided agent planning. the agent is provided with workflow knowledge in various formats and prompted to plan the next action. and multi-agent collaboration @xcite @xcite , the restricted scope of llms' intrinsic parametric knowledge can lead to the undesired phenomenon of planning hallucinations @xcite -llms can exhibit uncontrollable actions that conflict with task knowledge and such adverse effects obstruct their practical application in knowledge-intensive tasks. on the other hand, mitigating hallucinations by leveraging external knowledge is an ongoing key topic for llm research @xcite @xcite . as for llm-agent planning, some preliminary efforts have been made to enhance the planning quality of agents by incorporating workflow-related knowledge @xcite @xcite . for example, knowagent @xcite employs the explicit action knowledge summarized in natural language format to regulate the planning trajectory. proagent @xcite enhances the efficiency of complex tasks through control flows described in python code. despite the promise, these nascent works commonly shape workflow-related knowledge in miscellaneous and unmethodical formats, lacking in-depth exploration and rigorous benchmarking comparisons concerning the role of workflows. hence, it still remains underexplored how to formalize, utilize and evaluate such workflow-related knowledge for llm-based agents across different real-world scenarios. to this end, we meticulously explore different formats of workflow knowledge and establish a systematic benchmark for workflow-guided agent planning. our study initially revisits and formalizes various embedding formats of workflow knowledge, including natural language, symbolic code, and flowchart schema, as depicted in figure 1 . we dub this 'flowagent' and propose its formal notations. to perform a comparative evaluation, we present flowbench, the first comprehensive benchmark for workflow-guided agent planning. flow-bench covers an extensive taxonomy (6 domains, 22 roles, 51 scenarios) and different knowledge formats (text, code, flowchart) to synchronize with real-world applications. the benchmark data is constructed through a three-phase pipeline of task collection, workflow organization, and session generation. flowbench features numerous distinct characteristics, such as coverage, difficulty, expertlevel annotation, and support for multi-round useragent interaction, as summarized in to conduct a reliable assessment, we design a holistic evaluation framework that comprises two distinct granularities: (i)-static turn-level evaluation that focuses on single-step planning. (ii)-dynamical session-level evaluation that simulates sequential planning. based on such evaluation mechanisms, we evaluate llms across varying levels of capability and embedded with different formats of workflow knowledge. we observe that even the best-performing llm, @xcite , struggles to deliver satisfactory performance in certain tasks (43.2% and 40.9% success rate). by contrasting various workflow formats, we discover that flowcharts struck the best trade-off among performance, adaptability, and user-friendliness. all these results illuminate the profound challenge that flowbench offers to the present llm-agent designs and steer the focus of future exploration. ## flowbench the overview of its construction process and evaluation framework is displayed in figure 2 . in what follows, we will elaborate on this in detail. ## conclusions we introduce flowbench, the first benchmark tailored for evaluating workflow-guided agent planning. we first revisit and formalize different workflow knowledge formats. next, through extensive experiments on flowbench, we find that flowchart formats achieve the best trade-off in terms of performance and user experience. the results further indicate that even the best-performing model, gpt-4o, fails to deliver satisfying results on challenging flowbench. we hope that our work can provide meaningful insights to future research in the field of workflow-guided agent planning.