arXiv:2606.00135v1 Announce Type: new Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This paper studies tool-calling along two complementary axes: effectiveness, i.e., how this capability is measured, and efficiency, i.e., how it is learned. On effectiveness, we systematically analyze tool-calling evaluation pipelines and show that results can be highly sensitive to seemingly minor, often undocumented implementation choices including the random seed, system prompt, multi-turn template cons
This paper addresses critical challenges in the rapid development and deployment of LLM agents, focusing on the effectiveness of tool-calling and the efficiency of their training methods, which are becoming central to AI progress.
Understanding and standardizing the evaluation and training of tool-calling LLM agents is crucial for their reliable development and deployment across various industries, impacting the speed and quality of AI-driven automation.
The research highlights that current evaluation methods for tool-calling agents are highly sensitive to minor implementation choices, suggesting a need for more robust and systematic approaches to ensure consistent performance and reliable progress.
- · AI research institutions
- · LLM developers focused on agentic capabilities
- · Industries adopting AI automation
- · Companies developing robust AI evaluation platforms
- · Companies relying on ad-hoc LLM agent deployment
- · Developers with poor testing methodologies
- · Early, unstandardized AI agent solutions
Improved standardization and robustness in LLM agent development and evaluation.
Accelerated deployment of reliable AI agents across complex business processes, leading to increased automation and efficiency gains.
Ethical and safety concerns around autonomous AI agents become more easily addressed due to better understandability and control over their capabilities.
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