AI assistants are everywhere, but many still feel slow or disconnected during real conversations. I wanted to build an assistant that could listen, respond, and speak back in real time, without awkward pauses or delayed replies.

This article shares how I built a real-time AI assistant that supports both voice and text input, remembers conversations, and responds naturally.

The implementation is based on ZEGOCLOUD’s real-time communication and AI Agent capabilities, which handle low-latency audio streaming and conversational agent orchestration.

What I Wanted to Build

The goal was simple:

• Users can talk or type
• The assistant replies instantly
• Voice responses sound natural
• Conversations feel continuous

I did not want a request–response chatbot. I wanted something closer to a real conversation.

High-Level Architecture

The system has three main parts:

• A web frontend for chat and voice input
• A lightweight backend for authentication and session control
• A real-time AI agent that joins the conversation as a user

The backend stays minimal. Real-time audio streaming and message routing happen through the communication layer.

In this setup, ZEGOCLOUD handles real-time audio streaming, message delivery, and AI agent participation, while the backend focuses only on session control and security.

How the AI Agent Behaves

Instead of treating the AI as a service behind the scenes, the agent joins the chat room like a real participant.

When the user speaks:

• Audio streams to the agent
• Speech recognition runs in real time
• The language model processes intent
• The response streams back as voice

Interruptions matter. If the user speaks while the AI is talking, playback stops and control returns immediately. This single detail makes conversations feel much more natural.

Key Design Decisions

Streaming over batch processing

Waiting for full audio clips adds delay. Streaming ASR and streaming TTS reduce perceived latency and keep conversations fluid.

WebRTC for voice transport

WebRTC simplifies real-time audio handling in browsers and provides stable, low-latency connections.

Separate backend logic from real-time media

The backend manages sessions and authentication only. Audio and messaging stay on the real-time layer.

How to Build a Real-Time AI Assistant (Step by Step)

Step 1: Define the Interaction Model

Before writing code, decide how users interact with the assistant.

In this project, the assistant supports:

• Voice input through the microphone
• Text input through a chat interface
• Voice responses generated in real time

The AI joins the conversation as a participant instead of acting as a background service. This design simplifies message flow and enables natural turn-taking.

Step 2: Set Up the Real-Time Communication Layer

To support low-latency voice interaction, the app establishes a real-time audio connection between users and the AI agent.

At a high level:

• The browser captures audio from the microphone
• Audio streams to the real-time engine
• The AI agent publishes its own audio stream back to the room

WebRTC handles transport, echo control, and playback so the frontend stays lightweight.

In this project, the real-time layer is built on ZEGOCLOUD’s WebRTC-based infrastructure, which simplifies audio transport, echo control, and low-latency playback.

Step 3: Create and Configure the AI Agent

The AI assistant runs as an agent with three core capabilities:

• Speech recognition to convert voice into text
• Language processing to understand intent and generate replies
• Text-to-speech to speak responses naturally

During setup, the agent configuration defines:

• The system prompt (assistant personality)
• Language model parameters
• Voice settings for speech output
• Silence detection and interruption behavior

Once registered, the same agent configuration can serve multiple user sessions.

Step 4: Start a Conversation Session

Each chat session runs in an isolated room.

When a user starts a session:

• The backend creates a room and generates access tokens
• The frontend joins the room and publishes the user’s audio stream
• The backend creates an AI agent instance bound to that room

From this point, all messages and audio flow through the real-time channel.

Step 5: Handle Voice and Text Messages

The assistant treats voice and text inputs uniformly after transcription.

• Voice input becomes text through streaming ASR
• Text messages go directly into the conversation pipeline
• Both feed the same AI reasoning logic

Responses stream back as synthesized speech, allowing playback to start before the full reply finishes.

Step 6: Manage Interruptions and Turn-Taking

Natural conversations require interruption support.

If the user starts speaking while the AI is responding:

• Audio playback stops immediately
• Control switches back to speech recognition
• The assistant resumes listening without resetting context

This behavior removes awkward pauses and makes interactions feel human.

Step 7: Store Conversation Context

To maintain continuity:

• Recent messages are stored in a sliding window
• The assistant uses this context for follow-up responses
• Conversations persist locally, so users can resume sessions

This keeps memory lightweight while improving relevance.

Step 8: Build a Simple UI for Feedback

The UI shows:

• Message bubbles for the user and AI
• Current agent state (listening, thinking, speaking)
• Controls to switch between voice and text input

Clear visual feedback helps users trust the system and understand what the assistant is doing.

Final Thoughts

Real-time interaction changes how AI assistants feel. Once latency drops and interruptions work properly, conversations become far more natural. If you are building voice-based or multimodal AI applications, focusing on real-time behavior is just as important as model quality.

If you’re looking for a full, step-by-step implementation with detailed code examples, I’ve also written a more in-depth guide here: https://www.zegocloud.com/blog/build-an-ai-assistant