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Kuldeep Paul
Kuldeep Paul

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How to Build a Voice Agent: A Developer’s Guide to Real-Time AI Interviewers

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In recent years, the intersection of conversational AI and real-time audio technologies has reshaped the way interviews, customer support, and learning experiences are delivered. Voice agents—AI-powered entities capable of conducting dynamic conversations—are no longer a distant vision; they’re an achievable reality for developers and technical teams. This guide will walk you through the practical steps of designing, implementing, and monitoring a robust voice interview agent using modern tools, with a special focus on leveraging Maxim AI for observability and quality assurance.

Table of Contents

  1. Introduction: The Rise of AI Voice Agents
  2. Core Architecture and Technology Stack
  3. Prerequisites and Environment Setup
  4. Step-by-Step Implementation
    • Imports and Initialization
    • Event Instrumentation
    • Building the Interview Agent Class
    • Entrypoint and Session Management
    • Main Execution Block
  5. Observability and Debugging with Maxim
  6. Best Practices for Voice Agent Development
  7. Advanced Features and Next Steps
  8. Conclusion
  9. References and Further Reading

Introduction: The Rise of AI Voice Agents

Voice agents are transforming industries by providing scalable, intelligent, and context-aware interactions. From automating candidate interviews to powering customer support, these systems rely on real-time speech recognition, natural language understanding, and dynamic orchestration. Developers now have access to production-grade platforms like LiveKit for audio streaming and Maxim AI for agent observability and evaluation.

The value proposition is clear: voice agents can reference job descriptions, perform live web searches, and adapt their questions—all while logging each interaction for audit and improvement. For a deep dive into agent evaluation, see AI Agent Quality Evaluation.

Core Architecture and Technology Stack

A modern voice agent system typically comprises:

  • Audio Streaming: Real-time audio communication via platforms like LiveKit.
  • Conversational Agent: An AI model orchestrated to conduct interviews, ask relevant questions, and process answers.
  • Web Search Integration: Dynamic access to external information sources.
  • Observability Layer: Tools like Maxim AI for logging, tracing, and debugging agent actions.
  • Large Language Model (LLM): Models such as Gemini for understanding and generating natural language.
  • Speech-to-Text (STT) and Text-to-Speech (TTS): Transcription and voice generation capabilities.

For a technical comparison with other observability platforms, refer to Maxim vs LangSmith and Maxim vs Comet.

Prerequisites and Environment Setup

Before you begin, ensure you have the following:

  • Python 3.8+
  • LiveKit server credentials (URL, API key, secret)
  • Maxim account (API key, log repo ID)
  • Tavily API key for web search
  • Google Cloud credentials for Gemini LLM and voice

Environment Variables

Set up your .env file:

LIVEKIT_URL=https://your-livekit-server-url
LIVEKIT_API_KEY=your_livekit_api_key
LIVEKIT_API_SECRET=your_livekit_api_secret
MAXIM_API_KEY=your_maxim_api_key
MAXIM_LOG_REPO_ID=your_maxim_log_repo_id
TAVILY_API_KEY=your_tavily_api_key
GOOGLE_API_KEY=your_google_api_key
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Dependencies

Add to your requirements.txt:

ipykernel>=6.29.5
livekit>=0.1.0
livekit-agents[google,openai]~=1.0
livekit-api>=1.0.2
maxim-py==3.9.0
python-dotenv>=1.1.0
tavily-python>=0.7.5
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Initialize your virtual environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
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Create your project directory:

mkdir interview_voice_agent
cd interview_voice_agent
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Step-by-Step Implementation

Imports and Initialization

Begin by importing the necessary libraries and configuring your logger:

import logging
import os
import uuid
import dotenv
from livekit import agents
from livekit import api as livekit_api
from livekit.agents import Agent, AgentSession, function_tool
from livekit.api.room_service import CreateRoomRequest
from livekit.plugins import google
from maxim import Maxim
from maxim.logger.livekit import instrument_livekit
from tavily import TavilyClient

dotenv.load_dotenv(override=True)
logging.basicConfig(level=logging.DEBUG)

logger = Maxim().logger()
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
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This setup ensures your environment variables are loaded and your logging is ready for debugging and traceability. For more on setting up logging for AI agents, see LLM Observability: How to Monitor Large Language Models in Production.

Maxim Event Instrumentation

Instrument your agent for observability with Maxim:

def on_event(event: str, data: dict):
    if event == "maxim.trace.started":
        trace_id = data["trace_id"]
        trace = data["trace"]
        logging.debug(f"Trace started - ID: {trace_id}", extra={"trace": trace})
    elif event == "maxim.trace.ended":
        trace_id = data["trace_id"]
        trace = data["trace"]
        logging.debug(f"Trace ended - ID: {trace_id}", extra={"trace": trace})

instrument_livekit(logger, on_event)
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Maxim’s integration enables you to trace every agent action, making debugging and audit trails effortless. See Agent Tracing for Debugging Multi-Agent AI Systems for a detailed exploration.

Building the InterviewAgent Class

Define the agent responsible for conducting interviews:

class InterviewAgent(Agent):
    def __init__(self, jd: str) -> None:
        super().__init__(
            instructions=f"You are a professional interviewer. The job description is: {jd}\nAsk relevant interview questions, listen to answers, and follow up as a real interviewer would."
        )

    @function_tool()
    async def web_search(self, query: str) -> str:
        if not TAVILY_API_KEY:
            return "Tavily API key is not set. Please set the TAVILY_API_KEY environment variable."
        tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
        try:
            response = tavily_client.search(query=query, search_depth="basic")
            if response.get('answer'):
                return response['answer']
            return str(response.get('results', 'No results found.'))
        except Exception as e:
            return f"An error occurred during web search: {e}"
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This class is initialized with a job description and can perform live web searches to enrich the interview. For a comprehensive guide to prompt management, refer to Prompt Management in 2025: How to Organize, Test, and Optimize Your AI Prompts.

Entrypoint and Session Management

Handle room creation and launch the interview session:

async def entrypoint(ctx: agents.JobContext):
    print("\n🎤 Welcome to your AI Interviewer! Paste your Job Description below.\n")
    jd = input("Paste the Job Description (JD) and press Enter:\n")
    room_name = os.getenv("LIVEKIT_ROOM_NAME") or f"interview-room-{uuid.uuid4().hex}"
    lkapi = livekit_api.LiveKitAPI(
        url=os.getenv("LIVEKIT_URL"),
        api_key=os.getenv("LIVEKIT_API_KEY"),
        api_secret=os.getenv("LIVEKIT_API_SECRET"),
    )
    try:
        req = CreateRoomRequest(
            name=room_name,
            empty_timeout=600,
            max_participants=2,
        )
        room = await lkapi.room.create_room(req)
        print(f"\nRoom created! Join this link in your browser to start the interview: {os.getenv('LIVEKIT_URL')}/join/{room.name}\n")
        session = AgentSession(
            llm=google.beta.realtime.RealtimeModel(model="gemini-2.0-flash-exp", voice="Puck"),
        )
        await session.start(room=room, agent=InterviewAgent(jd))
        await ctx.connect()
        await session.generate_reply(
            instructions="Greet the candidate and start the interview."
        )
    finally:
        await lkapi.aclose()
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This logic prompts for a job description, creates a LiveKit room, and starts the agent session. For more on evaluation workflows, see Evaluation Workflows for AI Agents.

Main Execution Block 

if __name__ == "__main__":
    opts = agents.WorkerOptions(entrypoint_fnc=entrypoint)
    agents.cli.run_app(opts)
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This block ensures the script runs as a CLI application, ideal for developer workflows.

Observability and Debugging with Maxim

Maxim AI provides end-to-end observability for your agent:

  • Trace every prompt, response, and event
  • Monitor real-time performance
  • Audit for reliability and compliance
  • Debug multi-agent systems

For an in-depth look at reliability strategies, read How to Ensure Reliability of AI Applications: Strategies, Metrics, and the Maxim Advantage.

You can view all traces and logs in your Maxim dashboard, making it easy to iterate and improve agent behavior.

Best Practices for Voice Agent Development

  • Design for context-awareness: Use job descriptions and real-time search to keep conversations relevant.
  • Implement robust logging and tracing: Ensure every action is observable for debugging and compliance.
  • Handle errors gracefully: Provide informative messages for missing API keys or failed web searches.
  • Optimize prompts and instructions: Structure agent behavior with clear, goal-oriented instructions.
  • Test in real-world scenarios: Use mock interviews and real candidate data to validate performance.

For more on agent evaluation, see Agent Evaluation vs Model Evaluation: What's the Difference and Why It Matters.

Advanced Features and Next Steps

Once your basic voice agent is operational, consider these enhancements:

  • Multi-agent panel interviews: Deploy multiple AI personalities for comprehensive candidate assessment.
  • Performance scoring and feedback: Integrate real-time evaluation metrics for actionable insights.
  • Resume parsing integration: Personalize interview questions based on candidate resumes.
  • Code challenge capabilities: Embed technical assessments within the interview flow.
  • Emotion detection: Use vision models to gauge candidate stress and engagement.
  • Multi-language support: Expand reach for global talent acquisition.

For inspiration, explore Maxim’s case studies on scaling AI support, such as Comm100’s Workflow and Atomicwork’s Journey.

Conclusion

Building a voice agent is a multi-faceted engineering challenge, but with the right tools and frameworks, it becomes a rewarding project. By leveraging platforms like LiveKit for audio and Maxim AI for observability, developers can create intelligent, reliable, and scalable interview agents that set new standards for automation and user experience.

To get started, review the Maxim <> LiveKit Integration Docs and explore Maxim’s product features for deeper integration options.

References and Further Reading

For more hands-on tutorials and insights, visit the Maxim AI Blog and Maxim Docs.

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