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A voice agent listens for speech, decides when a turn begins and ends, interprets the request, and returns a spoken response. In a cascaded design, that path runs through voice activity detection (VAD), speech-to-text (STT), a language model (LLM), and text-to-speech (TTS). WebRTC may carry the live audio between a device and the agent; it handles media transport, not speech recognition or reasoning.
What happens when you talk to a voice agent?
A typical interaction has two connected paths: the agent processes what you say, then produces and plays a response. The details vary by architecture, but the main jobs are distinct.
- Capture: A browser or app obtains microphone audio, either through its built-in capture flow or an application-supplied media stream.
- Detect the turn: VAD detects speech activity, while turn detection or endpointing estimates whether you have started or finished speaking.
- Recognize speech: In a cascaded system, STT converts the audio into text.
- Interpret and respond: The LLM uses the recognized words and conversation context to formulate a response. It may also request an application tool; privileged actions should be authorized by trusted application logic, not by assuming a browser connection is secure.
- Generate speech: TTS converts the response text into audio. A speech-to-speech system can instead produce audio directly.
- Deliver the media: A transport such as WebRTC carries real-time audio. Other parts of the application may handle control events, tools, and business logic.
These are functional roles, not necessarily six separate services. Streaming can let one stage begin while another is still working, rather than making each wait for the previous stage to finish completely.
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VAD, or voice activity detection, identifies when audio contains speech. Turn detection uses that activity, along with timing or other signals, to decide when the user has begun a turn and when it is appropriate for the agent to answer. That decision is part of the conversation design: responding too early can cut a speaker off, while waiting too long can make the exchange feel sluggish.
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OpenAI Agents SDK guidance describes two server-side VAD approaches. server_vad is threshold-oriented and has configurable parameters; semantic_vad aims for more natural turn boundaries and may wait longer when a speaker sounds as though they have not finished. The right behavior depends on the interaction, not just the microphone signal.
STT, LLM, and TTS: the cascaded voice-agent pipeline
In a cascaded architecture, each named component has a separate job: STT supplies text, the LLM interprets and responds, and TTS speaks the generated text. Microsoft’s hosted-agent guidance describes this STT → LLM → TTS pattern and highlights the flexibility to choose text models and TTS voices separately.
That separation can make intermediate text and individual providers easier to inspect or customize. It also means the implementation must coordinate streaming, turn boundaries, audio playback, and interruptions across stages. If the stages run strictly one after another, each can add a wait; streaming and pipelining can overlap work, but actual end-to-end timing depends on the complete implementation.
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Speech-to-speech versus a cascaded pipeline
A speech-to-speech system takes audio in and produces audio out through a real-time model, without a separate STT-to-text and TTS chain. Neither approach is universally better: the useful choice depends on the conversation and how much control you need over its components.
| Decision axis | Cascaded STT → LLM → TTS | Speech-to-speech |
|---|---|---|
| Component choice | Separate recognition, text-model, and speech-synthesis components; Microsoft describes flexibility to choose text models and TTS voices. | A real-time model processes audio input and produces audio output without an intermediate STT/TTS chain. |
| Conversational dynamics | Modular stages must be coordinated for streaming, endpointing, playback, and interruptions. | Microsoft positions this pattern for natural dynamics such as interruptions and backchanneling when latency is critical. |
| Control and visibility | Separate stages make it possible to inspect and customize intermediate text and providers. | The model handles the audio interaction as a more unified path, with fewer separately managed stages. |
| Latency considerations | Serial processing can add stage waits; streaming can overlap work. Performance depends on the implementation. | It can avoid separate STT and TTS steps; OpenAI documents lower latency as a benefit of direct voice-to-voice interaction. |
Sources: Microsoft Learn and OpenAI Realtime conversations.
Why WebRTC matters: the media connection
WebRTC is the real-time media transport, not the agent’s VAD, STT, LLM, or TTS. OpenAI describes it as “an open standard for sending low-latency audio, video, and data between browsers, mobile apps, and servers.” Its practical value is that applications can rely on standardized media behavior instead of building a separate connectivity, encryption, codec, and adaptation layer for every client.
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WebRTC brings together several concerns that matter to live conversation: ICE helps establish connectivity across networks and NATs; DTLS/SRTP provides encrypted transport; codec negotiation lets endpoints agree how media will be encoded; and RTCP supports quality control. Client-side features such as echo cancellation and jitter buffering also help manage real-world audio conditions. These capabilities make WebRTC useful, but do not guarantee a fast or smooth conversation on their own. See OpenAI’s engineering explanation of low-latency voice infrastructure.
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Responsiveness depends on more than transport. OpenAI’s May 4, 2026 engineering article identifies low and stable media round-trip time, low jitter and packet loss, and fast connection setup as important to natural interaction. Endpointing, model response, synthesis, playback, and the network path all contribute to what a user experiences.
Choosing WebRTC, WebSocket, or SIP
The transport choice is partly a decision about who manages media capture, playback, and events. OpenAI’s Agents SDK documents these common paths:
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| Use case | Typical fit | What to plan for |
|---|---|---|
| Browser speech-to-speech | WebRTC, the documented low-friction browser SDK path when the application does not need to manage raw audio. | The SDK handles microphone capture, playback, and the WebRTC connection. |
| Browser audio with server-side controls | WebRTC can carry audio while the server owns session events, tools, and business logic. | Protect privileged actions with server-side authorization; browser-side peer-connection customization is not an access-control boundary. |
| Server-owned audio or custom pipeline | WebSocket when the application controls capture and playback and needs direct event access. | The application takes responsibility for managing audio and relevant events. |
| Telephony integration | SIP or a provider-specific bridge to connect voice sessions to phone systems. | Integration depends on the telephony provider and the chosen bridging setup. |
| Native mobile app | WebRTC can support real-time media, but the app may own more of the integration. | Account for app permissions, audio routing, transport, and lifecycle management. |
In Microsoft’s hosted-agent pattern, WebSocket signaling is distinct from the negotiated WebRTC media connection. That distinction is useful: a signaling or control channel and the path carrying live audio do not have to be the same connection. Read the OpenAI Agents SDK transport guide for its transport-specific guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How barge-in works
Barge-in is the ability for a user to speak while the agent is still talking. VAD can detect the new utterance, but a reliable interruption also needs playback cancellation and conversation history that reflects what the user actually heard. Otherwise, the agent may continue speaking over the user or retain unheard output as though it had been delivered.
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Handling differs by transport. With WebRTC and SIP, the Realtime server manages an output-audio buffer and can truncate audio that has not played. With WebSocket, the client owns playback and must stop it and send the appropriate truncation information. The Agents SDK describes transport-specific event flows: WebSocket handling includes listening for a speech-start event, truncating to the portion the user heard, and emitting an interruption event; WebRTC clears buffered output audio for the client. Consult the Realtime conversation guidance and the Agents SDK voice-agent guide rather than assuming one universal callback sequence.
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What latency figures can—and cannot—tell you
There is no universal acceptable-latency threshold established by these sources. One 2026 arXiv tutorial reports 947 ms P50 time-to-first-audio and a 729 ms best case for its particular implementation, which used streaming STT, a vLLM-served language model, and streaming TTS. Those are measurements from that configuration, not a general benchmark or a promise for another deployment. The paper is Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial.
For an implementation you are evaluating, consider the full interaction: time to establish a connection, how endpointing affects when processing begins, when audio generation starts, and whether the agent stops promptly when interrupted. A fast model alone cannot compensate for slow turn detection, an unstable media path, or playback that keeps running after the user starts speaking.
Putting the architecture together
For a browser-based conversational agent, a practical starting point is to decide first whether you want separate STT, LLM, and TTS components or a speech-to-speech model. Then select turn-detection behavior and a transport that matches who will own audio. WebRTC is a natural fit for browser or mobile real-time media; WebSocket is often appropriate when a server or custom client owns the audio pipeline; SIP is relevant when connecting to telephony. In every case, design interruption handling and server-side authorization as part of the system, rather than treating them as transport defaults.
For basic capture and testing, a built-in microphone may be sufficient; an external microphone is optional, not a voice-agent requirement.
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