To enhance large language models (LLMs) with real-world task-solving capabilities, integrating workflow knowledge into LLMs has emerged as a promising direction. However, real-world conversations are inherently dynamic—users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents FlowSwitch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of FlowSwitch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent’s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision (MAP). Moreover, FlowSwitch reduces nearly 50% of search operations, substantially lowering computational cost and response time.