Agentic workflows are commonly used to guide large language models in solving complex reasoning tasks.However, existing automated workflow generation methods primarily rely on stepwise local refinement or tree-based search over a single evolving workflow.Under limited optimization budgets, this paradigm constrains structural depth, hindering the discovery of workflows that require deep compositional structure.To address this limitation, we propose FUSIONFLOW, a framework centered on workflow fusion.Unlike incremental refinement, fusion enables structural leaps by synthesizing multiple independently evolved workflows, allowing exploration of deeper regions of the workflow space within a finite budget.To make fusion effective, FUSIONFLOW integrates local optimization, task-specific differentiation, and a dynamic scheduling mechanism.Experiments on six reasoning benchmarks demonstrate that FUSIONFLOW consistently outperforms existing automated workflow generation methods.Further ablation and analysis confirm that fusion is the key driver of deep structural exploration, highlighting fusiondriven exploration as an effective approach for overcoming depth limitations in automated workflow generation.