The most frustrating thing about speaking with a Voice AI agent is when it interrupts you, or it doesn’t understand the normal flow of human conversation. There are multiple ways to solve this depending on your use case, and coming up with the best architecture for your
When WebRTC quality degrades on your SFU (jitter spikes, frozen frames, choppy audio), your monitoring tells you something is wrong. It doesn’t tell you what is responsible. Is the network dropping packets? Or is your server too loaded to read them from the socket on time? The
SIP and RTP feed a call and its media into a voice AI pipeline. What happens next runs through the control plane: what the voice agent is told, and what it decides to do about it. In my talk at ClueCon this year, From SIP to Tokens:
Human speech is often unstructured and messy. To keep up with real conversation, Voice AI bots must parse partial sentences, filler words, and interruptions, all without the visual cues a human listener relies on. On top of that linguistic challenge, real-world latency budgets and network conditions test
