Migrating a textual content agent to a voice assistant is more and more vital as a result of customers anticipate sooner, extra pure interactions. As an alternative of typing, prospects need to converse and perceive in actual time. Industries like finance, healthcare, training, social media, and retail are exploring options with Amazon Nova 2 Sonic to allow pure, real-time speech interactions at scale.
On this put up, we discover what it takes emigrate a conventional textual content agent right into a conversational voice assistant utilizing Amazon Nova 2 Sonic. We evaluate textual content and voice agent necessities, spotlight design priorities for various use circumstances, break down agent structure, and tackle frequent issues like instruments and sub-agents for reuse and system immediate adaptation. This put up helps you navigate the migration course of and keep away from frequent pitfalls.
You may also discover a Ability within the Nova pattern repo that works with AI IDEs like Kiro and Claude Code to robotically convert your textual content agent right into a voice agent.
Textual content brokers and voice brokers aren’t the identical drawback
Whereas migrating from a textual content agent to a voice assistant may seem to be including a voice interface whereas maintaining the enterprise logic unchanged, it’s vital to grasp the variations from the next views.
Facet
Textual content agent
Voice agent
Person enter
Typed textual content: person reads, scrolls, copy-pastes at personal tempo
Spoken audio stream: actual time, can interrupt (barge-in), pauses matter
Response model
Paragraphs, lists, tables, hyperlinks: wealthy formatting, all information delivered without delay
Brief spoken phrases, one factor at a time: “Need me to proceed?” with affirmation loops
Latency price range
Mid-latency tolerance: typing indicator masks wait time
Extremely-low latency required: silence seems like one thing is damaged
Flip-taking
Strict request → response: person varieties, hits enter, waits
Fluid, overlapping, interruptible: voice exercise detection (VAD) + flip detection, barge-in required
Transport
HTTP / REST / Server-Despatched Occasions: stateless request-response
Bidirectional streaming: persistent connection, real-time audio in each instructions
To raised navigate these challenges, let’s break down the important thing variations between textual content brokers and voice assistants and the way these variations influence design and implementation.
Response design
A textual content agent is constructed to ship paragraphs that customers can learn at their very own tempo. Scrolling again, copying content material, and following hyperlinks as wanted. A voice agent operates in a basically totally different medium. Responses have to be conversational, concise, and punctiliously structured for listening quite than studying.Think about a banking agent that returns account info:
Textual content agent response:
This is your account abstract:
– Checking (****4521): $3,245.67
– Financial savings (****8903): $12,450.00
– Credit score Card (****2187): -$1,823.45 (fee due: March 15)
You may click on on any account for detailed transactions.
Voice agent response:
“You’ve three accounts. Your checking account ends in 4521 with a steadiness of three thousand 2 hundred forty-five {dollars}. Need me to undergo the others or would you want particulars on this one?”.
The voice agent breaks info into digestible chunks and asks for affirmation earlier than persevering with. It makes use of an autonomous dialog model, proactively guiding the person quite than dumping all the pieces without delay.
Latency price range
Textual content customers have mid-latency tolerance. They see a typing indicator and wait. Voice customers discover delays nearly instantly. Silence in a voice dialog seems like the road went useless. This adjustments how brokers have to be architected:
Issue
Textual content agent
Voice agent
Acceptable response time
Mid-latency tolerance: just a few seconds wait with a loading indicator is appropriate.
Low-latency tolerance: dialog needs to be within the a whole bunch of milliseconds, with first audio ASAP; delays of some seconds, particularly throughout instrument calls, really feel unresponsive.
Instrument name tolerance
A number of sequential calls OK
Every name provides noticeable silence
Streaming
Good to have
Important
Asynchronized instrument dealing with
Good to have
Important to have
Amazon Nova 2 Sonic helps asynchronous instrument calling, so the dialog continues naturally whereas instruments run within the background. It retains accepting enter, can run a number of instruments in parallel, and gracefully adapts if the person adjustments their request mid-process, delivering all outcomes whereas specializing in what’s nonetheless related.
Flip-taking and interruption
Textual content conversations are inherently turn-based. The person varieties, hits enter, waits for a response. Voice conversations are fluid. Customers interrupt (barge-in), pause mid-sentence, and anticipate the agent to deal with overlapping speech naturally.Native speech-to-speech fashions like Amazon Nova 2 Sonic deal with this internally with built-in voice exercise detection (VAD) and switch detection. Nova 2 Sonic manages dialog context with out requiring the complete historical past to be despatched on every flip.
Migration from an architectural view
With these variations in thoughts, let’s break down the migration from an architectural perspective by dividing the system into three main parts and analyzing how every evolves.A conceptual design of a textual content agent consists of three parts:
- A shopper utility (similar to net, cell, or IoT interfaces).
- A textual content orchestrator that manages the system immediate, instruments, and dialog context.
- The instrument integrations that hook up with your techniques, similar to APIs, databases, workflows, Retrieval Augmented Era (RAG) pipelines, or sub-agents.
When migrating this structure to a voice agent, these parts stay the identical, however every requires totally different adjustments to help voice-specific logic.
The shopper utility
Agent purchasers are usually applied in programming languages and techniques used for net browsers, cell apps, or IoT gadgets, relying on the deployment context.A voice agent shopper requires a persistent bidirectional connection (similar to WebSocket or WebRTC) and handles audio encoding/decoding, shopper occasions, barge-in logic, noise management, and transcription show. That is considerably extra advanced than a textual content shopper, which usually communicates with the agent by means of a stateless REST or one-way HTTPS streaming interface.
In consequence, this part often requires refactoring or a full rewrite. For instance, a PoC constructed with a Streamlit frontend would doubtless should be rebuilt utilizing a JavaScript framework like React to help bidirectional connections.
For a light-weight voice agent net shopper utility in REACT utilizing WebSocket, check with this pattern.
The orchestrator
An agent orchestrator is the central hub when constructing textual content or voice brokers. It manages the system immediate, selects and routes instruments or sub-agents, and maintains dialog context to maintain interactions coherent and aligned with the agent’s position. In textual content brokers, the orchestrator handles requests and responses between the shopper and the reasoning mannequin whereas integrating instruments to set off enterprise logic. Voice orchestrators comply with the identical ideas however add audio streaming, Voice Exercise Detection (VAD), Computerized Speech Recognition (ASR), reasoning, and Textual content-to-Speech (TTS). Amazon Nova 2 Sonic gives a bidirectional streaming interface that mixes these options, so customers can migrate reasoning prompts and gear triggers from textual content brokers for a smoother transition to voice.
One key distinction from a conventional text-agent structure is that Amazon Nova 2 Sonic can settle for each textual content and audio inputs in the identical mannequin interface. This implies Sonic can immediately substitute the standalone textual content reasoning mannequin usually utilized in a textual content orchestrator. As an alternative of chaining separate ASR → LLM → TTS parts, Sonic unifies speech recognition, reasoning, instrument use, and speech synthesis right into a single bidirectional mannequin. With this, groups can reuse current prompts and instruments whereas streamlining the structure, lowering latency, and eradicating the necessity to handle a separate textual content reasoning mannequin within the voice stack.
The next code snippets present a pattern textual content agent constructed with Strands Brokers utilizing Amazon Nova 2 Lite as the massive language mannequin (LLM). It has outlined instruments and a pattern utilizing Strands BidiAgent and Nova 2 Sonic to create a voice agent orchestrator accessible by means of WebSocket. You’ll discover that the coding model for each textual content and voice brokers in Strands is very comparable. Whereas the pattern makes use of Strands, the identical method applies to textual content brokers constructed with different frameworks similar to LangChain, LangGraph, or CrewAI, as a result of the important thing inputs required from the textual content orchestrator are the system immediate and gear definitions.
Earlier than working the samples within the following sections, set up Python and the required dependencies, together with strands-agents and Boto3, and ensure your IAM setup has the mandatory permissions for the required providers.
from strands import Agent, instrument
from strands.fashions import BedrockModel
# —- Mock instruments will likely be utilized in each textual content and voice brokers —-
@instrument
def authenticate_customer(account_id: str, date_of_birth: str) -> str:
“””Confirm buyer identification and return an auth token.”””
# In actual implementation, name your auth service / API
if account_id == “123456”:
return “AUTH_TOKEN_ABC123”
return “Authentication failed”
@instrument
def get_account_balance(auth_token: str) -> str:
“””Return the client’s present account steadiness.”””
if auth_token == “AUTH_TOKEN_ABC123”:
return “Your present checking account steadiness is $5,420.”
return “Unauthorized request”
@instrument
def get_recent_transactions(auth_token: str) -> str:
“””Return latest transactions.”””
if auth_token == “AUTH_TOKEN_ABC123”:
return “Current transactions: $45 groceries, $120 utilities, $18 espresso.”
return “Unauthorized request”
Utilizing Strands Brokers, you possibly can create a textual content agent orchestrator with Nova 2 Lite as proven within the following pattern:
# —- Nova 2 Lite mannequin —-
mannequin = BedrockModel(model_id=”amazon.nova-2-lite-v1:0″)
# —- Banking assistant textual content agent —-
bank_agent = Agent(
mannequin=mannequin,
system_prompt=”””You’re a banking assistant. Reply person questions on account balances, latest transactions precisely. At all times validate person identification earlier than offering delicate info.
“””,
instruments=[authenticate_customer, get_account_balance, get_recent_transactions],
)
Utilizing the Strands BidiAgent, you possibly can construct a voice agent orchestrator in an analogous coding model with the Nova 2 Sonic mannequin and reuse the identical instruments:
# voice_orchestrator.py — BidiAgent with sub-agents as instruments
from strands.experimental.bidi.agent import BidiAgent
from strands.experimental.bidi.fashions.nova_sonic import BidiNovaSonicModel
# —- Nova 2 Sonic mannequin —-
mannequin = BidiNovaSonicModel(
area=”us-east-1″,
model_id=”amazon.nova-2-sonic-v1:0″,
provider_config={“audio”: {“voice”: “tiffany”, “input_sample_rate”: 16000, “output_sample_rate”: 16000}},
)
# —- Banking assistant voice agent —-
agent = BidiAgent(
mannequin=mannequin,
system_prompt=””” You’re a banking assistant. Communicate naturally and reply questions on account balances, latest transactions. Affirm the client’s identification earlier than sharing delicate particulars. Use brief, clear responses and acknowledge when retrieving knowledge.
“””,
instruments=[authenticate_customer, get_account_balance, get_recent_transactions],
)
await agent.run(inputs=[ws_input], outputs=[ws_output])
The system immediate is the muse for each textual content and voice brokers. It defines the agent’s position, tone, and guardrails, guaranteeing responses are constant, dependable, and aligned with enterprise targets and person expectations throughout written and spoken interactions.When transferring from textual content to voice, adapt the system immediate for real-time audio. Maintain it concise and conversational, take into account latency and multi-turn context, and break advanced steerage into smaller steps.
Textual content immediate (authentic):
“You’re a banking assistant. Reply person questions on account balances, latest transactions precisely. At all times validate person identification earlier than offering delicate info.”
Voice-adapted immediate:
“You’re a banking assistant. Communicate naturally and reply questions on account balances, latest transactions. Affirm the client’s identification earlier than sharing delicate particulars. Use brief, clear responses and acknowledge when retrieving knowledge.”
Observe, that in a voice orchestrator with Nova 2 Sonic, you’re utilizing the Sonic built-in reasoning functionality to handle the system immediate and gear choice and session context. You not want to supply your individual LLM for reasoning on the orchestrator stage.
The enterprise logic layer
Instrument integration is a key facet of connecting an agentic assistant to the enterprise layer, utilizing protocols like Mannequin Context Protocol (MCP), Agent-to-Agent (A2A), and customary HTTP. In a text-based agent, the orchestrator sends textual content enter to instruments, like REST APIs, RAG system, or databases and receives textual content responses to generate user-facing replies.
Within the Strands Brokers samples, the identical instruments used for the textual content agent could be reused for the voice agent with no code adjustments. Nevertheless, reusing instruments and sub-agents for voice entails extra than simply implementation particulars.
Should you already use a multi-agent structure, your specialised enterprise logic brokers can usually be reused for voice with some updates. The next diagram exhibits a banking assistant the place a voice orchestrator calls sub-agents for authentication and mortgage inquiries.
Though these sub-agents don’t require a whole rewrite, they do want tuning for voice:
Shorter responses – a textual content sub-agent may return an in depth paragraph. A voice sub-agent ought to return 1–2 sentences that the orchestrator can converse naturally. For instance, you replace the sub-agent’s system immediate to say, “Summarize in 1 to 2 concise sentences” as an alternative of “Present a complete reply.”
Latency enchancment – select smaller, sooner fashions for sub-agents (for instance, begins from Nova 2 Lite as an alternative of a bigger mannequin). In a voice dialog, each additional inference hop provides noticeable silence. For Nova 2 Lite, we suggest limiting or keep away from utilizing considering mode, to scale back latency. For extra info, see the Amazon Nova Developer Information for Amazon Nova 2..
Lowered verbosity in instrument outcomes – some Sub-agents designed to return giant uncooked payloads, similar to JSON with extra knowledge than requested, leaving the orchestrator to filter the response. This isn’t supreme, particularly for voice. Bigger payloads improve latency, can cut back accuracy, and may expose delicate knowledge. Lean, focused responses are vital, significantly for latency-sensitive voice experiences.
Use filler messages to maintain conversations pure throughout longer instrument processing. With Amazon Nova 2 Sonic, you can also make asynchronous instrument calls and customise these interim messages, guaranteeing customers keep engaged whereas the agent completes duties.
Most of those changes contain immediate and configuration adjustments quite than architectural modifications. The sub-agent’s instruments, enterprise logic, and deployment stay the identical.Whereas sub-agent architectures present readability, reusability, and portability, and are particularly helpful when migrating a textual content agent to voice. Every sub-agent name provides latency as a result of its personal mannequin of inference and gear calls. In a voice dialog, this will translate to noticeable pauses for sub-agent causes.
Seek advice from this weblog for extra voice agent structure patterns and greatest practices for managing latency.
Conclusion
Migrating a textual content agent to a voice assistant isn’t a wrapper job. The interplay mannequin is basically totally different, from response design to latency budgets to turn-taking conduct. However with a well-structured multi-agent structure and Amazon Nova 2 Sonic, the enterprise logic layer stays intact.
Begin your migration venture and convert your textual content agent right into a voice assistant with Amazon Nova 2 Sonic. For a whole working instance of a voice agent utilizing Amazon Nova 2 Sonic, see the Amazon Nova 2 Sonic in Strands BidiAgent. Discover extra documentation and assets right here:
In regards to the authors
Lana Zhang is a Senior Specialist Options Architect for Generative AI at AWS inside the Worldwide Specialist Group. She focuses on AI/ML, with a give attention to use circumstances similar to AI voice assistants and multimodal understanding. She works intently with prospects throughout numerous industries, together with media and leisure, gaming, sports activities, promoting, monetary providers, and healthcare, to assist them rework their enterprise options by means of AI.
Osman Ipek is a Options Architect on Amazon’s AGI workforce specializing in Nova basis fashions. He guides groups to speed up improvement by means of sensible AI implementation methods, with experience spanning voice AI, NLP, and MLOps.
