Voice

Building Voice Applications with ElevenLabs Agents

Voice-based AI applications represent the next frontier in customer service training, offering a more natural and effective alternative to traditional methods. They create consistent, risk-free practice environments where trainees can master complex interactions before facing real customers.

To build great user experiences with these voice agents, there are three critical design principles to keep in mind

• Thoughtful Context Design: Creating authentic scenarios using real-world data like actual menus and service protocols
• Strategic Randomization: Implementing controlled variability that builds adaptability rather than rote responses
• Skill-Targeted Scenarios: Focusing on specific competencies rather than general conversation abilities

This article unpacks the practical lessons learned while building a voice-based training solution with ElevenLabs's new conversational AI that enables flight attendants to perfect premium cabin service interactions—without risking passenger satisfaction or airline reputation during the learning process.

Why are voice agents hard to build?

Voice interfaces feel inevitable: they promise hands‑free, universally accessible computing that matches the cadence of ordinary conversation. Yet building production‑grade voice agents remains stubbornly difficult.

This comes in the form of three specific constraints that make building these interfaces challenging

1. The strict 500ms response window to keep responses feeling natural that creates fundamental UX limitations
2. Complex end-to-end latency optimization challenges across the entire processing pipeline
3. The lack of established UX patterns for different conversational contexts