We present J-tau, the first publicly available Japanese benchmark for evaluating tool-using LLM agents in interactive customer-support scenarios.
While tau-bench is widely used to evaluate LLM agents, it is English-centric and cannot evaluate agents in other languages.
Existing multilingual extensions offer no public Japanese version and rely primarily on machine translation, leaving locale-specific entities such as addresses and currencies inconsistent with the target region.
To enable reliable evaluation in Japanese, we carefully localize tau-bench through machine translation, entity adaptation, and human review.
During this process, we also uncover and fix two flaws in the original tau-bench: invalid tasks and a score aggregation error that inflates certain models' results.
Experiments show that J-tau yields fewer evaluation errors than a naively machine-translated counterpart and that our fixes resolve both flaws.