Build an AI Voice Agent for Customer Service

Step-by-step guide to building an AI Voice Agent for customer service using VideoSDK. Includes complete code examples and testing instructions.

Introduction to AI Voice Agents in Customer Service

AI Voice Agents are transforming the way businesses handle customer interactions. These agents use advanced technologies to understand and respond to human speech, providing efficient and scalable customer service solutions. In this tutorial, we will explore how to build an AI

Voice Agent

 specifically designed for customer service using the VideoSDK framework.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software application that can understand and respond to human speech. It uses technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), and Language Learning Models (LLM) to process and generate human-like responses.

Why are they important for the Customer Service Industry?

AI Voice Agents are crucial in customer service for handling inquiries, providing information, and resolving issues efficiently. They operate 24/7, reduce wait times, and improve customer satisfaction by providing quick and accurate responses.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Language Learning Model): Processes the text to understand and generate responses.
  • TTS (Text-to-Speech): Converts the generated text response back into speech.
For a comprehensive understanding, refer to the

AI voice Agent core components overview

.

What You'll Build in This Tutorial

In this tutorial, you will build a fully functional AI

Voice Agent

 for customer service using Python and the VideoSDK framework. We will cover everything from setting up your environment to deploying and testing your agent.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI Voice Agent involves several components working together to process user input and generate responses. The data flow typically follows this sequence: user speech is captured and converted to text using STT, processed by the LLM to generate a response, and then converted back to speech using TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • CascadingPipeline: Manages the flow of audio processing from STT to LLM to TTS. Learn more about the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: Detects when the user has stopped speaking and when the agent should respond.

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and create an account on the VideoSDK platform at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage dependencies:
1python -m venv venv
2source venv/bin/activate  # On Windows use `venv\Scripts\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk
2pip install python-dotenv
3

Step 3: Configure API Keys in a .env file

Create a .env file in your project directory and add your VideoSDK API key:
1VIDEOSDK_API_KEY=your_api_key_here
2

Building the AI Voice Agent: A Step-by-Step Guide

Here is the complete code for the AI Voice Agent:
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import SileroVAD
4from videosdk.plugins.turn_detector import TurnDetector, pre_download_model
5from videosdk.plugins.deepgram import DeepgramSTT
6from videosdk.plugins.openai import OpenAILLM
7from videosdk.plugins.elevenlabs import ElevenLabsTTS
8from typing import AsyncIterator
9
10# Pre-downloading the Turn Detector model
11pre_download_model()
12
13agent_instructions = "You are an AI Voice Agent specialized in customer service. Your persona is that of a friendly and efficient customer service representative. Your primary capabilities include answering customer inquiries, providing information about products and services, assisting with order tracking, and resolving common issues. You can also escalate complex issues to human representatives when necessary. However, you must adhere to the following constraints: you cannot process payments or handle sensitive personal information, and you must always remind users to verify critical information through official channels. Additionally, you should maintain a polite and professional tone at all times."
14
15class MyVoiceAgent(Agent):
16    def __init__(self):
17        super().__init__(instructions=agent_instructions)
18    async def on_enter(self): await self.session.say("Hello! How can I help?")
19    async def on_exit(self): await self.session.say("Goodbye!")
20
21async def start_session(context: JobContext):
22    # Create agent and conversation flow
23    agent = MyVoiceAgent()
24    conversation_flow = ConversationFlow(agent)
25
26    # Create pipeline
27    pipeline = CascadingPipeline(
28        stt=DeepgramSTT(model="nova-2", language="en"),
29        llm=OpenAILLM(model="gpt-4o"),
30        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
31        vad=SileroVAD(threshold=0.35),
32        turn_detector=TurnDetector(threshold=0.8)
33    )
34
35    session = AgentSession(
36        agent=agent,
37        pipeline=pipeline,
38        conversation_flow=conversation_flow
39    )
40
41    try:
42        await context.connect()
43        await session.start()
44        # Keep the session running until manually terminated
45        await asyncio.Event().wait()
46    finally:
47        # Clean up resources when done
48        await session.close()
49        await context.shutdown()
50
51def make_context() -> JobContext:
52    room_options = RoomOptions(
53    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
54        name="VideoSDK Cascaded Agent",
55        playground=True
56    )
57
58    return JobContext(room_options=room_options)
59
60if __name__ == "__main__":
61    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
62    job.start()
63

Step 4.1: Generating a VideoSDK Meeting ID

To create a meeting ID, use the following curl command:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json" \
4-d '{}'
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class and defines the behavior of our AI Voice Agent. It uses the agent_instructions to guide its interactions, ensuring it acts as a friendly and efficient customer service representative.
1class MyVoiceAgent(Agent):
2    def __init__(self):
3        super().__init__(instructions=agent_instructions)
4    async def on_enter(self): await self.session.say("Hello! How can I help?")
5    async def on_exit(self): await self.session.say("Goodbye!")
6

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial as it manages the flow of data through the STT, LLM, and TTS plugins. Each plugin plays a specific role:
  • STT (DeepgramSTT): Converts speech to text using the "nova-2" model.
  • LLM (OpenAILLM): Processes the text using the "gpt-4o" model to generate responses.
  • TTS (ElevenLabsTTS): Converts the generated text back to speech using the "eleven_flash_v2_5" model.
  • VAD (SileroVAD): Detects voice activity to determine when to listen.
  • TurnDetector: Identifies when the user has finished speaking.
For more details on voice activity detection, refer to

Silero Voice Activity Detection

.
1pipeline = CascadingPipeline(
2    stt=DeepgramSTT(model="nova-2", language="en"),
3    llm=OpenAILLM(model="gpt-4o"),
4    tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
5    vad=SileroVAD(threshold=0.35),
6    turn_detector=TurnDetector(threshold=0.8)
7)
8

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and starts the interaction process. The make_context function sets up the room options for the session.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(
5        stt=DeepgramSTT(model="nova-2", language="en"),
6        llm=OpenAILLM(model="gpt-4o"),
7        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
8        vad=SileroVAD(threshold=0.35),
9        turn_detector=TurnDetector(threshold=0.8)
10    )
11    session = AgentSession(
12        agent=agent,
13        pipeline=pipeline,
14        conversation_flow=conversation_flow
15    )
16    try:
17        await context.connect()
18        await session.start()
19        await asyncio.Event().wait()
20    finally:
21        await session.close()
22        await context.shutdown()
23
24def make_context() -> JobContext:
25    room_options = RoomOptions(
26        name="VideoSDK Cascaded Agent",
27        playground=True
28    )
29    return JobContext(room_options=room_options)
30
31if __name__ == "__main__":
32    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
33    job.start()
34

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your AI Voice Agent, execute the following command in your terminal:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you will see a playground link in the console. Open this link in your browser to interact with the AI Voice Agent. You can speak to the agent and receive responses in real-time.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the agent's functionality by integrating custom tools using the function_tool feature. This allows you to add specific capabilities tailored to your needs.

Exploring Other Plugins

The VideoSDK framework supports various plugins for STT, LLM, and TTS. You can explore options like Cartesia for STT, Google Gemini for LLM, and other TTS providers to enhance your agent.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly set in the .env file and that you have the necessary permissions in the VideoSDK dashboard.

Audio Input/Output Problems

Check your microphone and speaker settings to ensure they are correctly configured and accessible by your system.

Dependency and Version Conflicts

Ensure all dependencies are installed in the virtual environment and that there are no version conflicts.

Conclusion

Summary of What You've Built

In this tutorial, you built a fully functional AI Voice Agent for customer service using the VideoSDK framework. You learned about the core components, set up the development environment, and created a custom agent class.

Next Steps and Further Learning

To further enhance your AI Voice Agent, consider exploring additional plugins and features offered by the VideoSDK framework. You can also delve into more complex scenarios and customizations to tailor the agent to specific business needs. For deployment details, refer to the

AI voice Agent deployment

 guide.

Get 10,000 Free Minutes Every Months

No credit card required to start.

Want to level-up your learning? Subscribe now

Subscribe to our newsletter for more tech based insights

FAQ