Voice agents are AI systems that conduct real-time spoken conversations, combining automatic speech recognition (ASR), large language model reasoning, and text-to-speech (TTS) synthesis into a seamless pipeline. By 2026, voice agents handle sales calls, customer support, appointment scheduling, and complex multi-turn dialogues with sub-second latency and human-like naturalness.

The standard voice agent pipeline processes audio in three stages:

1. ASR (Speech-to-Text) — Converts spoken audio to text in 100-300ms (Deepgram, Whisper, AssemblyAI)
2. LLM Reasoning — Processes the transcribed text, maintains conversation context, and generates a response in 200-500ms
3. TTS (Text-to-Speech) — Converts the response to natural-sounding audio in under 200ms (ElevenLabs, Deepgram Aura-2)

The total end-to-end latency target is under 800ms for natural conversational flow.

The OpenAI Realtime API bypasses the traditional pipeline by streaming audio directly to and from the LLM, enabling true speech-to-speech processing. This reduces latency by eliminating the ASR/TTS serialization steps and allows the model to use vocal cues like tone and emphasis.

import asyncio
import websockets
import json
 
async def voice_agent_realtime():
    url = "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "OpenAI-Beta": "realtime=v1"
    }
 
    async with websockets.connect(url, extra_headers=headers) as ws:
        # Configure the session
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "modalities": ["text", "audio"],
                "voice": "alloy",
                "instructions": "You are a helpful customer service agent.",
                "turn_detection": {"type": "server_vad"}
            }
        }))
 
        # Stream audio input and receive audio output
        async for message in ws:
            event = json.loads(message)
            if event["type"] == "response.audio.delta":
                play_audio_chunk(event["delta"])
            elif event["type"] == "response.text.delta":
                print(event["delta"], end="")

Voice agents connect to phone networks through SIP trunking and WebRTC:

Latency is the critical metric for voice agents. Key optimization strategies:

• Streaming responses — Begin TTS playback before the full response is generated
• Endpoint detection — Voice Activity Detection (VAD) determines when the user stops speaking
• Interruption handling — Agent stops speaking immediately when the user interjects
• Connection pooling — Maintain persistent WebSocket connections to avoid handshake overhead
• Edge deployment — Run ASR/TTS models closer to the user geographically
• Response caching — Cache common responses for instant playback

• ElevenLabs — Industry-leading TTS quality with 4.23 MOS scores, hyper-realistic voices, and multilingual support
• Vapi — Enterprise voice agent platform with 4,200+ configuration points, CRM integration, and global telephony
• Retell AI — Real-time adaptive voice agents with built-in analytics and no external carrier dependency
• PolyAI — Specialized in multilingual contact center agents with robust interruption handling

• Interruption handling — Natural conversations involve overlapping speech that agents must handle gracefully
• Accent and noise robustness — ASR must perform well across diverse speakers and environments
• Emotional intelligence — Detecting and responding appropriately to caller sentiment
• Multilingual code-switching — Handling conversations that switch between languages mid-sentence

• OpenAI Realtime API Guide
• AI Voice Agent Platforms Guide
• Deepgram Voice AI Buyers Guide 2026

• Agent Orchestration — Orchestrating voice agent workflows
• Function Calling — Tool calling during voice conversations
• Agent Memory Frameworks — Maintaining conversation memory across calls
• Agent Debugging — Observability for voice agent pipelines