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For a customer-support agent that needs natural spoken turns, interruptions, and realtime tool use, start with a speech-to-speech session using OpenAI’s Realtime API. Choose a chained speech-to-text, agent, and text-to-speech pipeline instead when you need to inspect or transform each stage independently. Then choose a browser or telephone connection path: they have different transport and operational demands.
Choose the voice architecture
The architecture determines how much of the speech workflow you can control. OpenAI describes two broad approaches: a realtime speech-to-speech session, or a chained pipeline that connects separate speech recognition, agent, and speech generation stages. Neither is a universal performance guarantee; choose according to the control your product needs.
| Architecture | Best fit | Tradeoff |
|---|---|---|
| Realtime speech-to-speech | Spoken conversation where immediate turns, interruptions, and realtime tool use matter. | Offers less stage-by-stage control than a chained pipeline. |
| Chained voice pipeline | Workflows where the team needs to inspect or transform recognized speech, agent output, or generated speech independently. | The application orchestrates speech recognition, agent processing, and speech generation as separate stages. |
OpenAI’s voice-agent guide explains the architecture options; its Realtime API getting-started guide covers realtime sessions.
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For a browser-based voice interface, the Realtime API can connect a client to a session over WebRTC or WebSocket. The documented browser setup has your application server create an ephemeral client secret; the browser uses that short-lived credential to connect. Do not put a standard API key in browser code.
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- Choose the first channel. Decide whether the initial experience is browser audio or telephone. The transport and operational constraints are different.
- Create the browser credential on your server. Follow the Realtime API setup to create an ephemeral client secret on the application server.
- Connect the client. Use WebRTC or WebSocket with the ephemeral credential. Pick the transport based on how much audio handling and event control your application needs.
- Set the server boundary for business operations. Keep access to customer records and sensitive business logic behind the appropriate server boundary. In particular, decide where the session runs before attaching function tools: tools run where the Realtime session runs.
- Build and test the support workflow. Exercise actual customer requests, including interruptions and handoffs, before release.
Choose WebRTC or WebSocket for a browser agent
The Agents SDK voice-agent guide describes WebRTC as the simpler browser route, with audio handling managed for you. WebSocket gives more direct control, but your application must manage audio capture and playback.
| Connection path | Use it when | What your team takes on |
|---|---|---|
| Browser WebRTC | You want the SDK-managed browser audio connection. | Designing the client/server boundary and deciding where session tools execute. |
| WebSocket or server-side transport | You need direct control over audio capture, playback, or events. | More responsibility for audio handling and interruption behavior. |
Transport choice does not remove the need to decide where application tools run. If the Realtime session runs in the browser, its function tools run there too; keep sensitive operations appropriately server-side.
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How do I connect a phone number to the Realtime API?
Telephone support needs a telephony connection, not just a browser audio session. OpenAI’s Telephony and SIP guide describes connecting a phone number to the Realtime API through SIP trunking and names Twilio as an example provider.
A separate option is the Agents SDK’s Twilio Media Streams extension: call audio is forwarded to a WebSocket server. Its guide is explicitly marked beta, so treat that status as applying to the cited documentation and verify the current status before relying on it for a launch. The extension documentation also calls out differences in phone-call latency and interruption timing; test those conditions on the actual phone path rather than assuming browser behavior will carry over.
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Evaluate the agent with repeatable support scenarios
Run scripted scenarios repeatedly so the team can compare behavior across changes. Include routine requests as well as conversations where a caller interrupts, changes their mind, or needs a human. OpenAI’s voice-agent guidance highlights these evaluation dimensions:
- Task completion: Did the agent resolve the request or reach the intended next step?
- Audible response latency: How long does the customer wait to hear a response?
- Interruptions: Can the customer interrupt, and does the agent respond appropriately?
- Unwanted silence: Are there gaps that make the conversation feel stalled?
Set acceptance thresholds for your own support workflows. The cited guidance does not define universal numeric targets, and the implementation sources establish no general latency, accuracy, savings, or deflection benchmark.
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