RectifyFlow: Plan Upfront, Rectify Selectively for Cost-Efficient Edge–Cloud Tool-Using Agents

Tool-using agents increasingly rely on cloud LLMs to execute complex multi-step workflows, but repeated LLM calls incur substantial monetary cost. Edge-side small models (SMs) offer a cheaper alternative, yet their weaker reasoning can compromise execution quality. Edge–cloud collaboration therefore promises a better quality–cost trade-off, but existing approaches remain inefficient for tool use: iterative execution triggers frequent cloud interactions, while local decision errors can propagate through dependent operations and cause costly replanning. We propose RectifyFlow, a cost-efficient edge–cloud framework that minimizes cloud involvement while containing edge-side execution errors. First, Dual-View Plan Construction produces structured plans that guide the edge SM through multiple dependent operations with limited cloud intervention, avoiding repeated cloud reasoning during execution. Second, when execution deviates from the plan, Dependency-Aware Plan Rectification identifies the deviation source, traces its downstream impact, and selectively repairs only the affected plan components, rather than regenerating the entire plan. Across BFCL and ToolSandbox, RectifyFlow retains 94.6% and 91.8% of Cloud-only performance while reducing cloud API cost by approximately 2.5$\times$ and 2.1$\times$, respectively. Against nine representative baselines, RectifyFlow achieves approximately 1.5$\times$ higher task performance at 1.3$\times$ lower cost, demonstrating a substantially improved quality–cost trade-off for tool-using agents.