Build AI Call Center Software with Voice Agents

Step-by-step guide to building an AI Voice Agent for call center software using VideoSDK.

Introduction to AI Voice Agents in AI Call Center Software

AI Voice Agents are revolutionizing the way call centers operate by automating customer interactions and providing efficient solutions to common queries. These agents use advanced technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLM) to understand and respond to customer inquiries.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software system designed to interact with users through voice. It listens to user inputs, processes the information using natural language understanding, and responds appropriately. These agents can handle a variety of tasks, from answering frequently asked questions to guiding users through complex processes.

Why are they important for the AI call center software industry?

In the call center industry, AI Voice Agents are crucial for improving efficiency and customer satisfaction. They reduce wait times by handling routine inquiries and can operate 24/7, providing consistent support. This allows human agents to focus on more complex issues, enhancing overall productivity.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Models (LLM): Processes and understands the text to generate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.
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 an AI

Voice Agent

 using the VideoSDK framework. This agent will be capable of understanding customer inquiries related to call center software and providing insightful responses.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

 involves several components working together. When a user speaks, the audio is captured and processed through a series of steps: Speech-to-Text conversion, text processing using a language model, and finally, Text-to-Speech conversion to deliver the response.
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: These components help the agent determine when to listen and when to speak. Explore the

    Turn detector for AI voice Agents

     for more details.

Setting Up the Development Environment

Prerequisites

To build your AI Voice Agent, ensure you have Python 3.11+ installed and a VideoSDK account, which you can create at app.videosdk.live.

Step 1: Create a Virtual Environment

Open your terminal and 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-agents videosdk-plugins
2

Step 3: Configure API Keys in a .env file

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

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

Here is the complete code for building your 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 specializing in 'ai call center software'. Your persona is that of a professional and efficient call center assistant. Your primary capabilities include handling customer inquiries, providing information about call center software features, assisting with troubleshooting common issues, and guiding users through software setup processes. You are also capable of escalating complex technical issues to human support agents when necessary. Your constraints include not being able to provide personalized technical support or access sensitive customer data. You must always remind users to verify any critical information with official documentation or support channels. Additionally, you should not attempt to make sales or offer pricing information, as these are outside your scope."
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 generate a meeting ID, use the following curl command:
1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: YOUR_API_KEY" \
3-H "Content-Type: application/json" \
4-d '{"region": "sg001"}'
5
This command will return a meeting ID that you can use to join a session.

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define your agent's behavior. It inherits from the Agent class and uses the agent_instructions to guide interactions. The on_enter and on_exit methods define what the agent says at the start and end of a session.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is central to processing audio data. It consists of:
  • DeepgramSTT: Converts audio to text.
  • OpenAILLM: Processes text to generate responses.
  • ElevenLabsTTS: Converts text responses back to audio.
  • SileroVAD: Detects voice activity to manage when the agent listens. For more on this, see

    Silero Voice Activity Detection

    .
  • TurnDetector: Helps manage conversational turns.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session, creating a CascadingPipeline and managing the session lifecycle. The make_context function sets up the room options, and the if __name__ == "__main__": block starts the agent.

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your 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'll find a

playground link

 in the console. Use this link to join the session and interact with your AI Voice Agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend your agent's functionality by integrating custom tools using the function_tool feature, allowing for more tailored responses and actions.

Exploring Other Plugins

The VideoSDK framework supports various plugins for STT, LLM, and TTS, enabling you to customize your agent's capabilities further.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file and that you have the necessary permissions.

Audio Input/Output Problems

Check your microphone and speaker settings and ensure they are configured correctly.

Dependency and Version Conflicts

Ensure all dependencies are up-to-date and compatible with your Python version.

Conclusion

Summary of What You've Built

In this tutorial, you've built a fully functional AI Voice Agent capable of handling inquiries related to call center software.

Next Steps and Further Learning

Explore additional plugins and customization options to enhance your agent's capabilities and learn more about the VideoSDK framework.

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