[2603.19896] Utility-Guided Agent Orchestration for Efficient LLM Tool Use

Computer Science > Artificial Intelligence arXiv:2603.19896 (cs) [Submitted on 20 Mar 2026] Title:Utility-Guided Agent Orchestration for Efficient LLM Tool Use Authors:Boyan Liu, Gongming Zhao, Hongli Xu View a PDF of the paper titled Utility-Guided Agent Orchestration for Efficient LLM Tool Use, by Boyan Liu and 2 other authors View PDF HTML (experimental) Abstract:Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance at the expense of excessive tool calls, longer trajectories, higher token consumption, and increased latency. In this paper, we study agent orchestration as an explicit decision problem rather than leaving it entirely to prompt-level behavior. We propose a utility-guided orchestration policy that selects among actions such as respond, retrieve, tool call, verify, and stop by balancing estimated gain, step cost, uncertainty, and redundancy. Our goal is not to claim universally best task performance, but to provide a controllable and analyzable policy framework for studying quality-cost trade-offs in tool-using LLM agents. Experiments across direct answering, threshold control, fixed workflows, ReAct, and several policy variants show that explicit orchestration signals substantially affect agent behavior. Additional analyses on cost definitions, workflow fairness, a...