Utility-Guided Agent Orchestration for Efficient LLM Tool Use
arXiv:2603.19896v1 Announce Type: new 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 workflow
arXiv:2603.19896v1 Announce Type: new 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.
Executive Summary
This article presents a novel approach to agent orchestration for Large Language Model (LLM) tool use. The authors propose a utility-guided orchestration policy that balances estimated gain, step cost, uncertainty, and redundancy to select among actions such as responding, retrieving, tool calling, verifying, and stopping. Through experiments and analyses, the study demonstrates the substantial impact of explicit orchestration signals on agent behavior and highlights the potential of lightweight utility design for controllable and analyzable agent control. By providing a framework for quality-cost trade-offs, this research contributes to the development of more efficient and practical LLM agent tools.
Key Points
- ▸ Utility-guided orchestration policy balances estimated gain, step cost, uncertainty, and redundancy
- ▸ Experiments demonstrate substantial impact of explicit orchestration signals on agent behavior
- ▸ Lightweight utility design provides a controllable and analyzable policy framework for agent control
Merits
Strength
The authors provide a theoretically sound and experimentally validated approach to agent orchestration, addressing a critical challenge in LLM tool use.
Strength
The study offers a unique framework for quality-cost trade-offs, enabling researchers and practitioners to design more efficient and practical LLM agent tools.
Demerits
Limitation
The proposed utility-guided orchestration policy may not generalize to all LLM tool use scenarios, particularly those with unique task requirements or constraints.
Limitation
The study relies on a limited set of experiments and analyses, which may not fully capture the complexities of real-world LLM agent applications.
Expert Commentary
The article presents a significant contribution to the field of agent orchestration for LLMs, addressing a critical challenge in LLM tool use. The authors' theoretically sound and experimentally validated approach offers a unique framework for quality-cost trade-offs, enabling researchers and practitioners to design more efficient and practical LLM agent tools. However, the study's limitations, such as potential non-generalizability and reliance on a limited set of experiments, should be acknowledged and addressed in future research. Ultimately, the proposed utility-guided orchestration policy has the potential to significantly impact the development of more effective LLM agent tools, with broader implications for AI system deployment and regulation.
Recommendations
- ✓ Future research should focus on expanding the scope of the proposed utility-guided orchestration policy to address additional LLM tool use scenarios and constraints.
- ✓ The study's findings should be further explored and validated through more extensive experiments and analyses, incorporating diverse task requirements and constraints.
Sources
Original: arXiv - cs.AI
