Finding the Edges: Testing, Evaluating, and Monitoring Voice AI Agents Before Your Users Do

Every demo of a voice AI agent looks great — until real users start talking. They interrupt, mumble, switch languages mid-sentence, and ask the one question that sends the agent off the rails. Traditional scripted QA only verifies the behaviors you already thought of, which is exactly why so many voice agents fail in production in ways their teams never saw coming. This session walks through a practical, engineering-grade approach to shipping Voice AI agents you can trust, built on three pillars: simulation, evaluation, and monitoring. You'll see how synthetic "drivers" — AI-powered simulated users with distinct personalities, goals, and contexts — hold realistic multi-turn conversations with your agent across 30+ languages and real-world audio conditions (noise, crosstalk, clipping), actively exploring the edges scripted tests miss. We'll then look at how judge-based, symbolic, and audio evaluations turn those discoveries into CI/CD release gates, so a change that degrades conversation quality fails the build before it reaches users. Finally, we'll close the loop with production monitoring that captures real failures and automatically converts them into regression tests — so the same mistake never ships twice. You'll leave with a concrete blueprint for finding the edges of your Voice AI agent before your customers do.