Workflow Extraction is the process of identifying and reconstructing structured sequences of tasks, actions, and dependencies from unstructured or semi-structured data sources such as text, logs, or recordings. It applies techniques from natural language processing, pattern recognition, and graph construction to infer procedural relationships and execution order. This process is used in automation, process mining, and knowledge discovery to model workflows for analysis, optimization, and system design.

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Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement

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Automatically extracting workflows as procedural graphs from natural language is promising yet underexplored, demanding both structural validity and logical alignment. While recent large language models (LLMs) show potential for procedural graph extraction, they often produce ill-formed structures or misinterpret logical flows. We present text2flow, a multi-agent framework that formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement. The framework iterates through three stages: (1) a graph extraction phase with the graph builder agent, (2) a structural feedback phase in which a simulation agent diagnoses and explains structural defects, and (3) a logical feedback phase in which a semantic agent aligns semantics between flow logic and linguistic cues in the source text. Important feedback is prioritized and expressed in naturallanguage, which is injected into subsequent prompts, enabling interpretable and controllable refinement. This modular design allows agents to target distinct error types without supervision or parameter updates. Experiments demonstrate that text2flow achieves substantial improvements in both structural correctness and logical consistency over strong baselines.

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