FlowAgent: Achieving Compliance and Flexibility for Workflow Agents
Yuchen Shi, Siqi Cai, Zihan Xu, Yuei Qin, Gang Li, Hang Shao, Jiawei, Chen, Deqing Yang, Ke Li, Xing Sun

TL;DR
FlowAgent introduces a framework that combines natural language and code to enable LLM-based workflow agents to maintain procedural compliance while flexibly handling unexpected queries, improving real-world automation.
Contribution
We propose FlowAgent, a novel framework with Procedure Description Language (PDL) that balances compliance and flexibility in LLM workflow agents, and a new evaluation methodology for OOW query handling.
Findings
FlowAgent effectively manages out-of-workflow queries.
It maintains high compliance with predefined procedures.
Experiments show improved flexibility and adherence across datasets.
Abstract
The integration of workflows with large language models (LLMs) enables LLM-based agents to execute predefined procedures, enhancing automation in real-world applications. Traditional rule-based methods tend to limit the inherent flexibility of LLMs, as their predefined execution paths restrict the models' action space, particularly when the unexpected, out-of-workflow (OOW) queries are encountered. Conversely, prompt-based methods allow LLMs to fully control the flow, which can lead to diminished enforcement of procedural compliance. To address these challenges, we introduce FlowAgent, a novel agent framework designed to maintain both compliance and flexibility. We propose the Procedure Description Language (PDL), which combines the adaptability of natural language with the precision of code to formulate workflows. Building on PDL, we develop a comprehensive framework that empowers LLMs…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Scientific Computing and Data Management
MethodsSparse Evolutionary Training
