Outbound Call AI: Build a Real-Time Voice Agent Using VideoSDK and Twilio

What is Outbound Call AI?

• Sales and lead generation
• Customer support
• Appointment reminders
• Follow-ups or surveys
• Real-time information dispatch

Why Use VideoSDK for AI Voice Agents?

• Join SIP calls and access live audio streams
• Native Python SDK for meetings and participant handling
• Event-driven architecture for stream and meeting events
• Great for plugging in speech-to-text, LLM, and TTS systems

Architecture Overview: AI Voice Agent System

1. Twilio handles telephony and connects phone calls to a SIP URI.
2. FastAPI receives Twilio webhooks and generates SIP instructions.
3. VideoSDK creates a meeting (room) for each call and gives us access to audio streams.
4. AI Agent listens to the audio, processes it, and can respond (optional).

• Caller → Twilio → FastAPI webhook → VideoSDK room → Agent joins room → Agent listens → (Future) Agent speaks back

Setting Up the Backend – main.py Walkthrough

1. FastAPI + CORS + .env config

1from fastapi import FastAPI
2from fastapi.middleware.cors import CORSMiddleware
3
4app = FastAPI()
5app.add_middleware(
6    CORSMiddleware,
7    allow_origins=["*"],
8    allow_credentials=True,
9    allow_methods=["*"],
10    allow_headers=["*"],
11)

2. Twilio Webhook to Handle Inbound Calls

1@app.post("/join-agent")
2async def handle_twilio_call(request: Request):
3    # Validate request
4    # Create VideoSDK room
5    # Start agent in background
6    # Return TwiML to dial SIP

3. Create a VideoSDK Room via API

1async def create_videosdk_room():
2    headers = {"Authorization": VIDEOSDK_AUTH_TOKEN}
3    async with httpx.AsyncClient() as client:
4        response = await client.post("https://api.videosdk.live/v2/rooms", headers=headers)
5        return response.json().get("roomId")

4. Generate TwiML to Connect SIP Call

1response = VoiceResponse()
2dial = response.dial()
3dial.sip(f"sip:{room_id}@sip.videosdk.live", username=..., password=...)
4return Response(content=str(response), media_type="application/xml")

Building the Agent – agent.py

1. Initialize the Agent and Join the Room

1class VideoSDKAgent:
2    def __init__(self, room_id, videosdk_token):
3        self.room_id = room_id
4        self.videosdk_token = videosdk_token
5        self.meeting = None
6        self._initialize_meeting()

2. Handle Meeting and Participant Events

Meeting Events:

1class AgentMeetingEventHandler(MeetingEventHandler):
2    def on_meeting_joined(self, data): ...
3    def on_participant_joined(self, participant): ...
4    def on_meeting_left(self, data): ...

Participant Events:

1class AgentParticipantEventHandler(ParticipantEventHandler):
2    def on_stream_enabled(self, stream): ...
3    def on_stream_disabled(self, stream): ...

3. Placeholder: Processing the Audio

1if stream.kind == "audio" and not self.participant.local:
2    # TODO: Start STT + LLM + TTS processing
3    logger.info("Received audio stream from participant")

• Convert audio → text (with OpenAI Whisper or Deepgram)
• Run AI response logic using LLM (GPT, Claude, etc.)
• Convert response text → audio (TTS from Google, Azure, ElevenLabs)

Sample Response Flow (Future Implementation)

1. Transcribe with Whisper
2. Pass to LLM
3. Generate a TTS audio stream
4. Inject that audio back into the room using VideoSDK’s custom_microphone_audio_track option

Use Cases for AI Voice Agents

• Sales Assistants: Automated cold calls with dynamic responses
• Support Bots: Voice-first helpdesks for tier-1 questions
• Reminder Systems: Appointments, billing, or re-engagement calls
• Voice Surveys: Dynamic call-based forms powered by AI

Scaling Your Voice AI

• Deploy with Railway, Render, or AWS
• Use Docker and gunicorn for performance
• Add Redis to manage agent state
• Monitor audio events with observability tools
• Add fallback flows for failed SIP or stream connections