Scaling voice agents breaks in a different place at each layer — here's the one that usually caps you first
Summary
This article discusses the challenges of scaling voice agents, noting that failures occur at different layers, and identifies the most common bottleneck that limits performance first.
Similar Articles
Your voice agent's biggest latency isn't always the model
Voice agents often face latency issues not mainly from the model but from components like endpointing; optimizing VAD and using proper benchmarking can reduce turn latency effectively.
Building voice AI agents that take turns like humans — the gotchas nobody warns you about
This article shares hard-won lessons from building real-time voice AI agents, highlighting the importance of proper turn-taking, VAD handling, billing awareness, and avoiding echo loops.
Building an AI voice agent from scratch: the parts that actually took our time
The author shares a postmortem on building a production phone-based AI voice agent, revealing that most engineering time was consumed by telephony infrastructure, turn detection, observability, and failure handling rather than core LLM behavior. They suggest using managed platforms like Vapi, Retell, or Dasha from the start to focus engineering effort on business logic.
Where does voice-agent latency still hide after you fix streaming?
The article discusses hidden sources of latency in voice-agent systems after streaming is enabled and seeks advice on which performance metrics are crucial for real-time agents.
I analysed 10000 voice ai call and 40% of them had similar problems
An analysis of 10,000 voice AI calls reveals key failure points: STT error rates, first-8-second chaos, interruption handling, extended silence, tool call latency, LLM failures, and broken escalation — offering practical insights for agent builders.