arXiv:2605.00737v2 Announce Type: replace Abstract: Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool when performing a task. This decision is particularly challenging for web search tools, where the benefits of external information depend on the model's internal knowledge and its ability to integrate potentially noisy tool responses. We introduce a principled framework i
The paper addresses a critical challenge in the rapidly evolving field of AI agents, where practical deployment necessitates more robust and efficient tool-calling mechanisms.
Improving LLM tool-calling efficiency and reliability directly impacts the performance, safety, and economic viability of autonomous AI systems, which are increasingly central to enterprise operations.
This framework offers a principled method to evaluate and optimize when LLMs should engage external tools, potentially leading to more deliberate and effective agentic AI behavior rather than indiscriminate tool use.
- · AI platform developers
- · Enterprises adopting AI agents
- · Developers of specialized AI tools
- · AI ethics and safety researchers
- · Inefficient AI agent architectures
- · LLMs without robust tool-calling mechanisms
- · Developers of redundant or harmful AI tools
More sophisticated and reliable AI agents will emerge, capable of navigating complex tasks with reduced error rates.
This improved reliability could accelerate the integration of AI agents into critical business processes, shifting white-collar workflows more rapidly.
Enhanced agentic AI could drive a new wave of automation, creating competitive advantages for early adopters and potentially restructuring entire industries.
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Read at arXiv cs.AI