On the Robustness of Agentic Function Calling (2025)

Abstract: Large Language Models (LLMs) are increasingly acting as autonomous agents, with function calling (FC) capabilities enabling them to invoke specific tools for tasks. While prior research has primarily focused on improving FC accuracy, little attention has been given to the robustness of these agents to perturbations in their input. We introduce a benchmark assessing FC robustness in two key areas: resilience to naturalistic query variations, and stability in function calling when the toolkit expands with semantically related tools. Evaluating best-performing FC models on a carefully expanded subset of the Berkeley function calling leaderboard (BFCL), we identify critical weaknesses in existing evaluation methodologies, and highlight areas for improvement in real-world agentic deployments.

## introduction large language models (llms) are reshaping artificial intelligence, shifting from static language processors to dynamic, task-oriented agents capable of planning, executing, and refining their actions. these agents hold the potential for transformative applications across various domains, including healthcare @xcite , finance @xcite @xcite , education @xcite , and customer support @xcite . llm agents have been revolutionarily positioned as routing systems that can act independently, make decisions and perform tasks with minimal human intervention. agentic function calling function calling (fc), the process by which an agent autonomously selects and invokes a specific function to retrieve information or execute a task, serves as a fundamental building block of an agentic system. in this context, a full execution trajectory can be seen as a complex, multi-turn (i.e., involving user interaction) sequence of function calls, ultimately achieving a given goal. models specifically optimized for fc are typically designed to generate a function call in response to a natural-language user request @xcite @xcite . the function (also known as a tool) is chosen from a predefined "toolkit"-a compact set of function descriptions foot_0 -provided as part of the model's prompt. the agent is expected to produce a syntactically correct tool invocation, ensuring that parameter values are appropriately assigned to function arguments (a process known as slot filling). for instance, given the query, "what is the record for the highest number of points scored by a single player in an nba game?" and the compact json tool description in figure 1 (top), the model is expected to generate the invocation code shown in figure 1 (bottom). several datasets and evaluation methodologies have been proposed to assess llms' function calling capabilities @xcite , and various benchmarks have been created for evaluating a range of fc scenarios, bfcl leaderboard @xcite among the most prominent ones. ## dataset generation we next provide details on the generation of our benchmark dataset. specifically, we describe the creation of (1) meaning-preserving rephrasings of user requests and (2) expanding the toolkit to include a set of semantically related tools. ## conclusions and future work we focus on two aspect of robustness, capturing input variations that can be expected in real-world agentic deployments: (1) meaning-preserving rephrasings of user requests and (2) agent's toolkit expansion to include a set of semantically related tools that are likely to be shortlisted by a selection module. we build a benchmark dataset, evaluate the robustness of several sota llm agents, and discuss the breakdown of failures. our future work includes testing the robustness of agentic fc with additional and diverse datasets. moreover, it has been shown that llms can be easily distracted by larger context @xcite . we plan to extend the set of experiments to scenarios were agent's toolkit is expanded also with non-relevant tools, to compare the performance against the current setting.