Where Context Lives in a Cascading Voice Agent — and Why the STT Layer Quietly Decides Your Accuracy

This story was originally published on HackerNoon at: https://hackernoon.com/where-context-lives-in-a-cascading-voice-agent-and-why-the-stt-layer-quietly-decides-your-accuracy. Voice agents fail when speech-to-text gets context wrong. Here’s why STT quality matters more than most teams realize. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #voice-ai, #ai-voice-agent, #voice-agents, #speech-to-text, #cascading-agents, #turn-detection, #universal-3.5-pro, #good-company, and more. This story was written by: @assemblyai. Learn more about this writer by checking @assemblyai's about page, and for more stories, please visit hackernoon.com. A cascading voice agent chains speech-to-text, an LLM, and text-to-speech — and every layer downstream can only react to the transcript it's handed. So your real accuracy ceiling is set at the STT layer, not the LLM. This piece maps the four places context lives in the stack, shows how to feed the agent's own turns back into Universal-3.5 Pro Realtime with agent_context on LiveKit and Pipecat, and makes the case that most teams pick their STT last when they should pick it first.