AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios
arXiv:2605.27995v1 Announce Type: new Abstract: Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response latency, and are usually limited to single-task settings. In real-world applications, multiple tasks often need to be executed concurrently, and overall efficiency depends on whether an agent can use idle time while waiting for tool responses. We refer to this capability as asynchronous tool calling. To evaluate
The rapid development of LLMs and their integration into agentic systems necessitates more sophisticated evaluation metrics, particularly as these systems move towards real-world, multi-task applications.
This research addresses a critical gap in AI agent evaluation by focusing on asynchronous capabilities, which are essential for efficient and robust performance in complex, dynamic environments.
The explicit evaluation of asynchronous tool calling capabilities will accelerate the development of more efficient and capable AI agents, shifting focus from raw tool-use ability to optimization of parallel operations.
- · AI agent developers
- · Cloud computing providers
- · Businesses adopting AI agents for workflow automation
- · Legacy process automation systems
- · Developers focused solely on sequential task execution
Improved efficiency and throughput of AI agent-driven workflows.
Broader adoption of AI agents in complex multi-task scenarios across various industries.
Enhanced AI agent capabilities could reduce the need for human oversight in certain operational roles, impacting labor markets.
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Read at arXiv cs.AI