Bridging the Gap: Migrating Text Agents to Voice Assistants with Amazon Nova 2 Sonic

Transforming User Interactions: The Shift from Text to Voice

Understanding the Unique Challenges of Voice Migration

Key Differences Between Text and Voice Agents: What You Need to Know

Designing for Different Modalities: Response Design and Latency

Enhancing Turn-Taking and Interruption Management in Voice Interactions

Architectural Migration: Adapting Components for Voice Technology

Client Application Development: From Text to Voice

The Role of the Orchestrator in Voice Agent Architecture

Reusing Business Logic: Integrating Tools and Sub-Agents

Ensuring Natural Conversations: Managing Latency and Tool Responses

Conclusion: A Roadmap for Successful Migration to Voice Assistants

Meet the Authors: Experts in AI and Voice Technologies

Migrating Text Agents to Voice Assistants: Unlocking Natural Interactions with Amazon Nova 2 Sonic

In today’s fast-paced digital landscape, users expect quicker, more intuitive interactions. The rise of voice assistants reflects this demand for natural, real-time communication. Industries such as finance, healthcare, education, social media, and retail are increasingly leveraging solutions like Amazon Nova 2 Sonic to facilitate seamless voice interactions. This blog post delves into the essential processes involved in transitioning from a traditional text agent to a conversational voice assistant and how to navigate common challenges.

Understanding the Differences: Text Agents vs. Voice Assistants

Migrating a text agent to a voice assistant involves more than simply adding a voice interface. The operational dynamics vary significantly. Here’s a breakdown of key differences:

Response Design: Crafting for Listening

Text agents are built for readability, allowing users to scroll and extract information at their discretion. In contrast, voice agents must articulate responses in concise, digestible chunks, guiding users through interactions.

Text Agent Response Example:

"Here’s your account summary:

• Checking (****4521): $3,245.67
• Savings (****8903): $12,450.00
• Credit Card (****2187): -$1,823.45 (payment due: March 15)."

Voice Agent Response Example:

"You have three accounts. Your checking account ends in 4521 with a balance of three thousand two hundred forty-five dollars. Want me to go through the others or would you like details on this one?"

Latency and Turn-Taking

Voice users have a low tolerance for latency; silence disrupts the conversational flow. Therefore, architecting voice interactions requires consideration of real-time processing and asynchronous tool handling, enabling continuous conversation even during backend operations.

Architecting the Migration

The architecture for transitioning to a voice agent involves three main components:

1. Client Application:

   • Traditional text clients operate via REST or one-way HTTPS. Voice clients, however, need persistent bidirectional connections to handle audio events and transcription display.

2. Agent Orchestrator:

   • This central hub manages system prompts, tool routing, and conversation context. While implementing voice interaction, it requires additional features such as Voice Activity Detection (VAD) and integration with Text-to-Speech (TTS) and Automatic Speech Recognition (ASR).

3. Tool Integrations:

   • Both text and voice agents utilize backend tools for business logic. Voice tools must be optimized for shorter responses and reduced latency to enhance user experience.

Example Code for Tool Integration

Utilizing libraries like Strands Agents, you can create both text and voice agents effectively. For instance:

Text Agent Code Snippet:

from strands import Agent, tool 

@tool 
def get_account_balance(auth_token: str) -> str: 
    return "Your current checking account balance is $5,420." 

model = BedrockModel(model_id="amazon.nova-2-lite-v1:0") 

bank_agent = Agent( 
    model=model, 
    system_prompt="You are a banking assistant.",
    tools=[get_account_balance], 
) 

Voice Agent Code Snippet:

from strands.experimental.bidi.agent import BidiAgent  
from strands.experimental.bidi.models.nova_sonic import BidiNovaSonicModel  

model = BidiNovaSonicModel(  
    region="us-east-1",  
    model_id="amazon.nova-2-sonic-v1:0",
)  

agent = BidiAgent(  
    model=model,   
    system_prompt="You are a banking assistant. Speak naturally.",
    tools=[get_account_balance], 
)  
await agent.run(inputs=[ws_input], outputs=[ws_output]) 

Adapting System Prompts

When transitioning, the system prompts must also shift towards a verbalizing tone. A voice-adapted prompt focuses on clarity, brevity, and the expectation of natural dialogues.

Original Text Prompt:

"You are a banking assistant. Always validate user identity before providing sensitive information."

Voice-Adapted Prompt:

"You are a banking assistant. Speak naturally and confirm the customer’s identity before sharing sensitive details."

Challenges and Solutions

While migrating, it’s crucial to be aware of common pitfalls:

1. Concurrency: Use Amazon Nova 2 Sonic’s asynchronous capabilities to allow smooth conversation flow while carrying out backend tasks.
2. Response Tuning: Optimize tool outputs to deliver concise information and avoid overwhelming users with details.
3. Latency Management: Reduce unnecessary processing steps that could delay responses and negatively impact user experience.

Conclusion

Migrating from a text agent to a voice assistant is not simply a systems upgrade; it’s a transformation that requires meticulous planning and design adjustments. By leveraging Amazon Nova 2 Sonic and adhering to best practices, organizations can transition smoothly while maintaining robust business logic.

For a hands-on demonstration, check out the Amazon Nova 2 Sonic repository, where you can find a sample skill that works seamlessly with AI tools like Kiro and Claude Code.

About the Authors

Lana Zhang is a Senior Solutions Architect specializing in Generative AI at AWS, focusing on AI voice assistants across various industries.
Osman Ipek is a Solutions Architect on Amazon’s AGI team, guiding teams in voice AI and NLP solutions.

Explore the resources and documentation available to set your migration journey in motion!