aiqbench analysis
This research addresses the critical efficiency bottleneck in tool-using LLM agents by framing orchestration as an explicit decision problem rather than relying solely on prompt engineering. The proposed utility-guided policy framework balances execution costs against answer quality, offering a controllable mechanism to mitigate excessive token consumption and latency inherent in free-form reasoning methods like ReAct. For developers building scalable AI systems, this work provides a defensible approach to managing the trade-off between performance and resource expenditure in complex agent workflows.
Utility-Guided Agent Orchestration for Efficient LLM Tool Use
arXiv:2603.19896v2 Announce Type: replace 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, and redundancy control further demonstrate that lightweight utility design can provide a defensible and practical mechanism for agent control.
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, and redundancy control further demonstrate that lightweight utility design can provide a defensible and practical mechanism for agent control.
Artificial Intelligence (cs.AI)
arXiv:2603.19896 [cs.AI]
(or arXiv:2603.19896v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.19896 Focus to learn more arXiv-issued DOI via DataCite
2026 IEEE International Conference on Joint Cloud Computing (JCC), Fukuoka, Japan, 2026, pp. 333-338
https://doi.org/10.1109/JCC72984.2026.00053 Focus to learn more DOI(s) linking to related resources
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