AI

Arabic prompts, English tools: mind the gap in AI agents

Kari Jaaskelainen

10 Jan 2026 — 1 min read

Arabic AI users are hitting a hidden speed bump

LLM-powered agents are everywhere—but most tests are English-first. Kubrak et al. present the first benchmark to evaluate tool-calling and agentic skills when users prompt in Arabic.

What they built: a standardized way to measure functional accuracy and robustness in Arabic agent workflows.
Key finding: a consistent 5–10% drop in tool-calling accuracy when interactions are in Arabic—even if tool descriptions are Arabic or English.
Why it matters: Arabic users face hidden performance gaps that can derail real automations.

Bottom line: If your product serves Arabic speakers, don’t assume English evals transfer. Evaluate and train for Arabic agent workflows.

Read more: https://arxiv.org/abs/2601.05101v1

Paper: https://arxiv.org/abs/2601.05101v1

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AI LLM Arabic NLP Agents ToolUse Benchmark LanguageEquity

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