Overview

Voice AI demos are everywhere. The caller says the perfect thing, the agent responds perfectly, and the demo ends before anything unexpected happens.

Production is different.

In real deployments, speech recognition mishears callers, APIs timeout, conversations wander off script, and latency starts breaking the experience. The gap between a working demo and a reliable voice system is where most projects fail.

In this SignalWire LIVEWire webinar, Brian West (resident AI expert and Head of Developer Experience) walks through five engineering patterns that repeatedly break voice AI in production based on real systems teams have built, deployed, and struggled to maintain.

If you're building or evaluating voice AI, this session will help you understand what actually fails once the calls start coming in and how to design systems that hold up under real usage.

What You'll Learn
During this 30-minute session, we'll break down five common architectural patterns that cause voice AI systems to fail in production:

• Prompt-Stuffed Architecture: Why putting business logic inside prompts leads to inconsistent results and hallucinated behavior.
• Implicit State Machines: What happens when conversation flow lives inside an LLM instead of an explicit state model.
• Ignoring the Voice Latency Budget: Why a few seconds of silence destroys the caller experience and how to measure the full voice pipeline.
• Tool Calls as an Attack Surface: How giving LLMs too many responsibilities creates unpredictable system behavior.
• Zero Post-Call Observability: Why transcripts alone aren't enough to debug AI agents and what production telemetry actually needs to capture.
  

You’ll also see the architectural patterns used to solve these problems, including state-governed agents, constrained tool calls, and real observability for AI-driven voice systems.

Reserve Your Spot Now
Join us for a technical walkthrough of the patterns that cause voice AI systems to fail and the architecture that prevents them.