SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages

The Southeast Asian Blind Spot in AI Agent Evaluation

The release of SEATauBench marks a necessary correction to a growing imbalance in AI evaluation. While benchmarks like GAIA, ToolBench, and AgentBench have become standard for measuring tool-use and agent capabilities, they overwhelmingly operate in English and a handful of high-resource languages. This new benchmark, detailed in the arXiv paper, extends the TauBench evaluation framework into seven Southeast Asian languages including Thai, Vietnamese, Indonesian, and Filipino.

The core contribution is straightforward: SEATauBench adapts the existing multi-step, tool-augmented question-answering tasks from TauBench into languages spoken by over 600 million people. This is not a superficial translation exercise. The benchmark requires models to understand culturally grounded queries, retrieve information from local sources, and execute tool calls in the target language—all while maintaining the multi-hop reasoning chains that make agent evaluation challenging.

Why This Matters Beyond Regional Politics

The paper’s reference to "sovereign AI" is not rhetorical. Southeast Asian governments and enterprises are investing heavily in local language models, from Singapore’s SEA-LION to Vietnam’s PhoGPT and Indonesia’s IndoBERT. However, these efforts have lacked standardized evaluation for agentic capabilities—the ability to use tools, browse the web, and execute multi-step tasks. Without SEATauBench, developers were essentially flying blind, relying on translated versions of English benchmarks that miss linguistic nuances and local knowledge requirements.

Consider a practical example: a Thai-language agent asked to "find the cheapest flight from Bangkok to Chiang Mai next Friday, then book it with a credit card." This requires parsing Thai date formats, understanding local airline websites, and executing payment tool calls—all in context. English benchmarks cannot capture this complexity.

Implications for AI Practitioners

For developers building multilingual agents, SEATauBench provides three immediate benefits:

First, it establishes a baseline for tool-use performance across SEA languages, enabling direct comparison between models. Early results suggest significant performance gaps, with even frontier models struggling on tasks that require deep local knowledge.

Second, the benchmark’s design reveals where models fail. Is it the language understanding itself, or the tool-calling logic? By separating these dimensions, practitioners can target their fine-tuning efforts more precisely.

Third, and perhaps most importantly, SEATauBench sets a precedent for other under-resourced language communities. The methodology—adapting an existing agent benchmark through careful translation, cultural adaptation, and tool integration—is reproducible. We can expect similar efforts for African, South Asian, and indigenous languages in the coming year.

The broader lesson is that agent evaluation cannot remain monolingual. As AI systems increasingly act on behalf of users—booking travel, managing finances, controlling smart homes—they must do so in the user’s language and cultural context. SEATauBench is a small but significant step toward that reality.

Key Takeaways

• SEATauBench adapts the TauBench agent evaluation framework into seven Southeast Asian languages, filling a critical gap in multilingual AI assessment
• The benchmark tests both linguistic understanding and tool-use capabilities in culturally relevant contexts, revealing performance gaps that English-only evaluations miss
• For AI practitioners, SEATauBench provides actionable baselines for fine-tuning and identifies specific failure modes in multilingual agent systems
• The methodology is reproducible and likely to inspire similar benchmarks for other under-resourced language communities globally