A method called EDGEGEN generates synthetic edge cases to improve tool-calling LLM agents beyond happy-path scenarios, according to the research. The approach targets the difficulty of obtaining high-quality, diverse task datasets for evaluating and optimizing tool-calling agents in enterprise applications, where privacy and other constraints limit real data. Existing synthetic task generation methods often produce limited results, the source states.
Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diverse task datasets that are often difficult to obtain due to privacy and other constraints. Existing synthetic task generation methods often produce g
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