Building a conversational AI agent involves a complex, real-time pipeline: converting user speech to text, processing it with a large language model, and generating a spoken response. The challenge lies in making this intricate sequence feel instantaneous and natural to the user, especially when conversations cross language barriers. This entire process must execute with minimal delay.
Thor Schaeff of ElevenLabs detailed this architecture in a workshop at the AI Engineer World's Fair, providing a practical look at creating agents that can seamlessly switch languages. He framed the core challenge as an interconnected system of specialized components. "We have the user who is speaking some language... we need to transcribe that speech into text. We then feed that into a large language model, which is kind of acting as the brain of our agent," Schaeff explained. This modular approach separates speech recognition (ASR) from language understanding (LLM) and speech generation (TTS), allowing developers to select and optimize each component. While the LLM handles the core logic and reasoning, platforms like ElevenLabs provide the high-fidelity audio bookends for the interaction.
The true complexity emerges with multilingual support. An effective global agent must not only understand different languages but also detect which one a user is speaking and adapt instantly. This requires more than simple translation; it involves reconfiguring the entire pipeline on the fly. If a user switches from English to Mandarin, the agent must not only prompt the LLM in the new language but also switch to a corresponding Mandarin voice for its response. This dynamic capability is what separates a robotic system from a fluid conversational partner.
ElevenLabs addresses this through a suite of tools that manage the agent's state and abilities. A key function is language detection, a system tool that "gives the agent the ability to change the language during conversation." When the system detects a switch, it can automatically select a pre-configured, appropriate voice for the new language. This enables a crucial layer of polish for creating believable, global-ready AI agents. It allows for hyper-localization, ensuring a user in Brazil hears a native Brazilian Portuguese accent, not a generic one, creating a more authentic and trustworthy user experience. The final output is a text-to-speech stream that completes the conversational turn, ready for the user's next input.
