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The Evolution of Tool Use in LLM Agents (2026)

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Unified survey from single-tool call to multi-tool orchestration — covers reasoning-time planning, training/trajectory construction, safety, resource efficiency, open-environment completeness, and benchmark design (HIT & Harvard)

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Open Source

About The Evolution of Tool Use in LLM Agents (2026)

A comprehensive survey paper that reviews the evolution of tool use in large language model (LLM) agents, from simple single-tool calls to complex multi-tool orchestration over long trajectories. The paper organizes recent literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation. It also summarizes representative applications in software engineering, enterprise workflows, graphical user interfaces, and mobile systems, and discusses major challenges and future directions for building reliable, scalable, and verifiable multi-tool agents.

Key Features

Comprehensive review of multi-tool LLM agents
Six core dimensions: inference-time planning, training/trajectory construction, safety/control, resource efficiency, open-environment completeness, benchmark design
Applications in software engineering, enterprise workflows, GUIs, and mobile systems
Identifies challenges and outlines future research directions
Unified task formulation distinguishing single-call from long-horizon orchestration

Pros & Cons

Pros
  • Comprehensive coverage of six critical dimensions in multi-tool agent research
  • Provides a unified framework for comparing different approaches
  • Includes practical application domains and future directions
  • Open-access on arXiv with detailed citations and references
Cons
  • Not a software tool but a research survey paper
  • May not include the very latest developments post-submission date (March 2026)
  • No implementation code or practical examples provided
  • Not peer-reviewed (arXiv preprint)

Best For

Research on LLM agent architectures and tool integrationDesigning and evaluating multi-tool agent systemsUnderstanding safety, cost, and verifiability constraints in agent deploymentsDeveloping benchmarks for tool-use capabilities in LLMsApplying multi-tool orchestration in software engineering and enterprise automation

FAQ

What is the main focus of this paper?
The paper surveys the evolution of tool use in LLM agents, shifting from single-tool calls to multi-tool orchestration over long trajectories, covering planning, training, safety, efficiency, and benchmarks.
Who are the authors of this paper?
The authors are Haoyuan Xu, Chang Li, Xinyan Ma, Xianhao Ou, Zihan Zhang, Tao He, Xiangyu Liu, Zixiang Wang, Jiafeng Liang, Zheng Chu, Runxuan Liu, Rongchuan Mu, Dandan Tu, Ming Liu, and Bing Qin.
Where can I access the full paper?
The paper is available on arXiv at https://arxiv.org/abs/2603.22862, with full PDF and HTML versions under an open-access license.
What subject areas does this paper belong to?
It is classified under Computer Science > Software Engineering (cs.SE) and also relates to Computation and Language (cs.CL).