The article discusses the tradeoff between latency and control in voice AI architectures, comparing traditional cascaded systems with end-to-end models, and seeks community input on current practices.
Hi there, I've been experimenting with voice agents recently and keep coming back to what seems like a pretty fundamental tradeoff: latency vs control. If you build the traditional pipeline: Speech → ASR → LLM → TTS → Speech you get a lot of control. You have text between each component, so you can inject context, filter responses, enforce policies, log everything, call tools, switch models, etc. The problem is latency. Even if every component is relatively fast, you're stacking multiple steps together. And voice is way less forgiving than chat. A 2-second pause in a text conversation is nothing. A 2-second pause on a phone call feels surprisingly long. End-to-end speech-to-speech models solve a lot of this. Audio in → audio out. The latency difference can be pretty dramatic and the conversation starts feeling much more natural. But then you lose a lot of the control surfaces you had before. This seems especially problematic for actual customer service agents. For example, I might want the agent to: identify the customer pull their order check a refund policy call an API decide whether a refund is allowed process it log what happened escalate if necessary With a cascaded system, I can inspect/control almost every step. With end-to-end speech-to-speech, I get much better conversational latency, but I have fewer places to reliably intervene. Fine-tuning an end-to-end model seems like one possible answer, but then you're adding training cost and a lot more engineering complexity. I've also seen people try to hide latency with things like: "Hmm, let me check that for you..." or typing/background sounds while tools are running. It helps, but IMO it doesn't really solve the underlying problem. My guess is that end-to-end eventually wins, but only when these models can reliably accept new context during the conversation and output structured data/tool calls alongside the voice. Curious what people actually building voice agents are doing today. Are you using ASR → LLM → TTS, end-to-end speech-to-speech, or some hybrid? And if you've tried both, how much control are you willing to give up to get sub-second latency? Any thoughts would be greatly appreciated!
The article discusses the underutilized potential of voice as an output layer for AI agents, highlighting practical use cases and workflow challenges beyond simple text-to-speech.
A detailed explainer on the five-layer architecture of AI voice agents, including speech-to-text, LLM, text-to-speech, orchestrator, and telephony, all operating under a 500ms latency constraint to maintain natural conversation flow.
A community discussion asking what people are using for AI voice agents in production, focusing on latency, interruption handling, and reliability, with mentions of LuMay Voice Agent, Vapi, Retell, and Twilio.
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.
The author compares platforms for orchestrating voice-enabled AI agents, evaluating features such as real-time interactions, workflow orchestration, and enterprise deployment, while seeking additional recommendations.