How to Build Production-Ready Voice Agents
Voice agents are transforming customer interactions, but most implementations fail at scale. Here's how to build systems that actually work in production.
The Challenge
Building a voice agent that works in a demo is easy. Building one that handles 10,000 concurrent calls with sub-300ms latency is hard.
What breaks at scale:
- Latency spikes during peak hours
- Memory leaks in long conversations
- Context loss across interruptions
- Poor handling of edge cases
Architecture Principles
Streaming-First Design
Don't wait for complete responses. Stream everything:
// Bad: Wait for full response
const response = await model.generate(prompt);
await tts.speak(response);
// Good: Stream tokens as they arrive
for await (const token of model.stream(prompt)) {
tts.speakChunk(token);
}
Stateless Call Handling
Store conversation state in Redis, not in memory:
interface CallState {
conversationId: string;
context: ConversationContext;
utterances: Utterance[];
sentiment: number;
}
// Store in Redis with TTL
await redis.setex(`call:${callId}`, 3600, JSON.stringify(state));
Graceful Degradation
When primary systems fail, fall back intelligently:
const providers = [openai, anthropic, localModel];
for (const provider of providers) {
try {
return await provider.generate(prompt);
} catch (error) {
console.warn(`Provider ${provider.name} failed, trying next`);
}
}
// Final fallback to scripted responses
return getScriptedResponse(intent);
Real Numbers
From our production deployments:
- Latency: p50 180ms, p99 420ms (first token)
- Uptime: 99.97% over 6 months
- Concurrent calls: 5,000+ peak
- Cost per call: $0.08 average
What's Next
This is just the foundation. In future posts we'll cover:
- Interrupt handling and conversation repair
- Multi-language support at scale
- Custom voice cloning for brand consistency
- Integration with CRM and ticketing systems
Need help building voice infrastructure? We've deployed systems handling 50K+ calls/month. Book a call to discuss your requirements.