Utility-Guided Agent Orchestration for Efficient LLM Tool Use
📰 ArXiv cs.AI
Utility-Guided Agent Orchestration balances answer quality and execution cost for LLM tool use
Action Steps
- Formulate agent orchestration as a decision problem
- Evaluate trade-offs between answer quality and execution cost
- Implement utility-guided orchestration to optimize tool usage
- Monitor and adjust the orchestration strategy to minimize latency and token consumption
Who Needs to Know This
AI engineers and researchers designing LLM systems benefit from this approach as it optimizes tool usage and improves overall efficiency
Key Insight
💡 Agent orchestration can be optimized using utility-guided decision-making to improve LLM tool efficiency
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💡 Balance answer quality & execution cost with Utility-Guided Agent Orchestration for LLM tool use
Key Takeaways
Utility-Guided Agent Orchestration balances answer quality and execution cost for LLM tool use
Full Article
Title: Utility-Guided Agent Orchestration for Efficient LLM Tool Use
Abstract:
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 le
Abstract:
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 le
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