How to build multilingual AI agents for customer support Twilio published a guide for building multilingual AI voice agents on its ConversationRelay product, which handles speech-to-text and text-to-speech while letting developers connect the LLM of their choice. The guide instructs developers to add one element per language a call may use, each mapping a language code to its own text-to-speech provider, voice, transcription provider and speech model, and to set the speech-to-text language to "multi" with Deepgram as the transcription provider so the system identifies the language of each utterance automatically. Twilio notes that all three layers of a phone conversation — speech-to-text, the LLM and text-to-speech — must switch together, since an LLM that handles Spanish still fails if speech-to-text is listening for English. How to build multilingual AI agents for customer support Time to read: Written by How to build multilingual AI agents for customer support introduction Language isn’t always linear. A customer might initiate a call in English but switch to Spanish midway through the conversation. It happens, but most voice agents keep right on answering in English. The caller repeats themselves or perhaps returns to a non-native English, or they ask for a human—either way, your AI agent has let them down. It doesn’t need to be this difficult, though. Nor do you need to hire dozens of bilingual agents or translate your entire help center. AI agents make multilingual customer support scalable for businesses of all sizes and budgets. A single agent can serve customers in many languages, but it needs to be able to detect when a caller switches languages and follow along when it does. Here's how to build an AI agent that detects a customer's language, switches mid-conversation, and remembers the preference for next time on the phone and in messaging . What is a multilingual AI agent? toc-heading-df6f017d-72f8-45f3-8bef-6d30f33a03dc A multilingual AI agent is an AI agent that detects and responds to customers in more than one language. Smart multilingual agents recognize which language a customer is using and switch its own language to match. This is simpler in messaging and chat functions. Modern LLMs read and write dozens of languages, so a text-based agent can usually respond in whatever language the customer writes in. Easy. But voice is harder. A phone conversation runs through three systems: 1. Speech-to-text https://www.twilio.com/en-us/speech-recognition turns the caller's voice into words 2. The LLM reads https://www.twilio.com/docs/glossary/what-is-a-large-language-model and decides what to say 3. Text-to-speech https://www.twilio.com/docs/voice/twiml/say/text-speech turns the reply back into audio The LLM might handle Spanish perfectly, but if speech-to-text is still listening for English, the transcript it hands the LLM won’t work. All three layers have to switch together. How to build a multilingual AI agent how-to-build-a-multilingual-ai-agent We’ll be using Twilio ConversationRelay https://www.twilio.com/en-us/products/conversational-ai/conversationrelay , which handles speech-to-text and text-to-speech for voice AI agents. It also lets you connect the LLM of your choice. First, you’ll configure languages before you detect them, and then you’ll need to detect them before you switch. 1. Choose speech providers that cover your languages toc-heading-df21c72b-2ae0-433f-9ac4-62c7dcd9b42d Every language your agent might hear needs a speech-to-text provider that can transcribe it and a voice that can speak it. Language availability, coverage, and quality vary depending on your provider, so start with the languages your customers speak, then double-check each one is supported. In ConversationRelay, you add one