Inzichten · Architecture

Arabic AI agents: what dialect-aware and RTL really require

Most AI vendors treat Arabic as a translation target. Customers experience it differently: they write in dialect, read right to left, and live on WhatsApp. Serving them well means treating those three things as the specification, not as edge cases.

1 min. leestijd

Dialect-aware, not just Modern Standard Arabic

Modern Standard Arabic is what models are mostly trained on; it is not what customers type. A customer in Cairo, Riyadh, or Dubai writes in their own dialect, and an agent that only understands MSA misreads intent exactly where service matters most.

Dialect-aware agents are tuned per market: retrieval, understanding, and the register of the reply all match how the customer actually writes and speaks.

A customer in Cairo, Riyadh, or Dubai writes in their own dialect, and an agent that only understands MSA misreads intent exactly where service matters most.

RTL as a designed layout, not a mirror

Flipping a left-to-right interface produces a wrong-feeling product: numerals, mixed-direction text, icons, and emphasis all break in small ways. Right-to-left has to be designed: typography chosen for Arabic, alignment and flow built for the direction, the same brand tokens carried over.

The same applies to the agent's output. Citations, confidence levels, and mixed Arabic-English content need bidirectional handling that was planned, not patched.

An Arabic-language chat interface, designed right to left
Right-to-left is a designed layout: typography, alignment and flow built for the direction, not a mirrored template.

The channel reality: WhatsApp and voice

WhatsApp is the customer-service channel across MENA, so the agent has to live there as a first-class citizen, with voice close behind: Agentforce Voice carries spoken Arabic where Salesforce runs the process.

And because Arabic service concentrates in regulated markets, deployment is private: retrieval and grounding run in your environment, and customer data never leaves it.

Questions

Asked and answered.

Can one agent serve Arabic and English customers?

Yes, and it should: one agent, many languages, consistent behaviour. The grounding and the action layer are shared; the language layer adapts to the customer.

What gets missed most often in Arabic AI projects?

Dialect and direction. Teams budget for translation and discover too late that MSA-only understanding misreads customers and that mirrored layouts feel broken. Both have to be in the spec from day one.

Slimmer werken, niet harder

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