Table of contents · 10 sections

Real-Time Voice AI Agents with ElevenLabs Conversational AI

Building interactive voice applications often forces developers to stitch together separate transcription models, language model inference, text-to-speech synthesis, and low-level WebRTC buffers—a fragile stack that frequently yields awkward delays and fractured turn-taking. ElevenLabs Conversational AI solves this architectural headache by wrapping automatic speech recognition, dynamic LLM orchestration, natural voice rendering, and proprietary interruption handling into a single managed runtime. With this approach, you can deliver sub-second, full-duplex voice interactions with custom voices and bidirectional client tools using only a few dozen lines of code.

Some links in this post are affiliate links; see our disclosure.

The Verdict: Pick It if / Skip It if

• Pick it if: You need production-grade voice agents with human-like vocal inflection, natural interruption handling, custom voices, and rapid front-of-house UI integration via React without maintaining custom WebRTC media servers.
• Skip it if: Your application requires completely offline or air-gapped execution, packet-level digital signal processing control, or commodity call center pricing below $0.03 per minute.

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What Is ElevenLabs Conversational AI?

Traditional conversational bots rely on a disjointed request-response loop: a user speaks, speech-to-text converts audio to strings, a language model generates a response, and text-to-speech converts those words back to audio. This cascaded setup introduces latency at every transition, struggles when users interrupt mid-sentence, and fails to replicate natural human dialogue.

ElevenLabs addresses this challenge with its Conversational AI platform (ElevenAgents), a managed infrastructure built specifically for bidirectional voice dialogue. Rather than requiring developers to coordinate three distinct vendor APIs, the platform packages four tightly coupled subsystems into a unified streaming session:

1. Automatic Speech Recognition (ASR): A streaming transcription engine optimized for rapid phonetic ingestion and noisy backgrounds.
2. Language Model Reasoning: Native integration with leading foundation models alongside support for custom endpoints via Bring-Your-Own-LLM (BYO-LLM) configurations.
3. Low-Latency Text-to-Speech (TTS): Over 10,000 synthetic and cloned voices across more than 70 languages, powered by streaming Flash and Turbo speech synthesis engines.
4. Turn-Taking & Acoustic Intelligence: A conversational turn model designed to identify natural pauses, distinguish between thinking sounds and completed thoughts, and process instantaneous barge-in interruptions.

According to the official ElevenLabs Agents Overview, the platform provides three deployment paths:

• ElevenAgents: The full omnichannel agent platform featuring visual workflow builders, knowledge base retrieval (RAG), conversation analytics, evaluation suites, and client/server tooling.
• Realtime Models: Standalone speech-to-speech models accessible directly via WebSocket or WebRTC, offering wire-protocol compatibility for teams migrating from the OpenAI Realtime API.
• Speech Engine: A modular audio transport layer designed for teams that already operate an established text chatbot backend and seek to bolt on conversational voice without rewriting existing dialogue graphs.

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Why Low-Latency Voice Architecture Matters Now

In voice interfaces, latency defines whether a user perceives the system as an intelligent partner or an irritating phone tree.

Human conversational dynamics operate on narrow timing margins. Telephony research establishes that the average gap between spoken turns in human conversation sits between 200 and 400 milliseconds. When an automated agent takes longer than 800 milliseconds to emit its first audio packet—known as Time-to-First-Audio (TTFA)—the conversation enters an uncanny valley. The human speaker assumes the system failed to hear them, begins repeating their statement, and immediately collides with the agent's delayed response.

Cascaded DIY Stack:
User Speaks ──>[ VAD ]──>[ STT (300ms) ]──>[ LLM TTFT (400ms) ]──>[ TTS TTFB (350ms) ]
Latency: 1200ms - 2200ms (High friction, awkward collisions)

ElevenLabs Conversational AI:
User Speaks ──>[ Streaming Turn-Taking + Acoustic Pipeline ]
Latency: 500ms - 800ms (Fluid, natural pacing)

Achieving sub-second response times in a DIY architecture requires substantial engineering overhead. As outlined in the LiveKit Voice Agent Documentation, a developer must deploy media servers, calibrate VAD thresholds to prevent cut-offs, stream partial LLM tokens into a speech generator, and manage jitter buffers. If your transcription provider experiences a 200ms network spike, your conversational timeline collapses.

ElevenLabs mitigates this fragility by co-locating transcription, reasoning, and speech synthesis within optimized edge datacenters. Persistent WebSocket or WebRTC connections eliminate repeated HTTP handshakes and provide built-in interruption handling that halts server-side playback the moment the user resumes speaking.

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Under the Hood: Architectural Anatomy and Turn-Taking

To achieve lifelike cadence, the architecture addresses three core mechanics: turn detection, interruption handling, and client tooling.

1. Acoustic Turn Detection vs. Energy-Based VAD

Simple voice bots rely on energy-based silence detection: if the user stops speaking for 700 milliseconds, the system assumes the turn is finished. This creates two distinct failure modes:

• Premature Cut-offs: If a user pauses mid-thought ("My order number is 84... um... 21"), energy-based VAD cuts them off and triggers an incorrect hallucination.
• Artificial Dead Air: Setting the silence threshold higher (e.g., 1200ms) to prevent cut-offs makes every normal turn exchange sluggish.

ElevenLabs employs an acoustic turn-taking model that evaluates vocal cadence, intonation drops, and linguistic completion markers rather than relying solely on raw decibel thresholds. This allows the system to hold the line during brief mid-thought pauses while answering promptly when a sentence reaches semantic closure.

2. Full-Duplex Interruption Handling (Barge-In)

True conversation is full-duplex: both parties can transmit audio simultaneously. When an agent is reciting a lengthy list and the user says "Choose the second option," a half-duplex system forces the user to wait until playback finishes.

Under the ElevenLabs architecture, the client maintains a constant upstream microphone stream while playing downstream audio. When user speech is detected during agent playback, the client-side audio player instantly flushes its hardware playback queue, and the server halts upstream LLM generation and audio rendering. This resets conversation state in milliseconds without desynchronizing the dialogue transcript.

3. Execution Context: Webhooks vs. Client Tools

Conversational systems frequently need to interact with external business logic. ElevenLabs partitions executable capabilities into two execution planes:

• Server Tools (Webhooks): HTTP endpoints invoked directly from ElevenLabs server infrastructure. These handle database lookups, CRM queries, or payment confirmations where backend secrets must remain isolated from the user.
• Client Tools: Functions executed directly within the user's client environment (browser or mobile app). Client tools allow the voice agent to trigger UI modals, update shopping carts, navigate pages, or read client-side sensors, returning data back to the agent in real time.

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Comparing Voice Agent Architectures: Managed vs. DIY vs. Native Realtime

When architecting a real-time voice application, engineering teams generally choose between three architectural paradigms:

1. Managed Conversational Platform (ElevenLabs Conversational AI)
2. Cascaded DIY Pipeline (LiveKit / Daily + Deepgram + Custom LLM + TTS API)
3. Native Speech-to-Speech Realtime Model (OpenAI Realtime API)

The table below summarizes the key trade-offs across these approaches:

Dimension	ElevenLabs Conversational AI	Cascaded DIY Pipeline (LiveKit + APIs)	OpenAI Realtime API (GPT-4o)
Complexity	Low (Single SDK, managed turn-taking)	High (Requires WebRTC SFU, audio workers)	Low to Medium (Single socket connection)
Latency (TTFA)	~500ms – 800ms	~1,100ms – 2,200ms (Tuning dependent)	~400ms – 700ms
Voice Realism	Industry-leading (10,000+ voices, emotional control)	Variable (Depends on chosen TTS provider)	Good (Synthetic standard presets)
Voice Cloning	Supported (Instant and Professional Cloning)	Feasible if routing to specialized TTS	Not supported (Fixed vendor voices)
UI Interactivity	Native React/Mobile SDKs with client tools	Requires custom signaling data channels	Requires custom WebRTC data channels
Barge-In Handling	Managed proprietary acoustic model	Manual calibration of VAD cancellation	Native to speech-to-speech architecture
Pricing	Subscription tiers + bundled minutes (~$0.08–$0.10 / min)	Split across 3+ vendors (~$0.05–$0.12 / min)	$0.06 / min in + $0.24 / min out ($0.30 / min)
Deployment	Managed cloud (US, EU, India residency)	Self-hosted workers or hybrid cloud	Managed cloud (OpenAI infrastructure)

While a cascaded DIY pipeline provides granular control over individual components, the operational burden of monitoring three separate services, handling network edge disconnections, and tuning VAD across differing microphones makes it difficult to maintain in production. Conversely, while OpenAI's Realtime API offers competitive speed, its lack of custom voice cloning and higher runtime costs (~$0.30 per minute total conversational time) make it restrictive for branded deployments. ElevenLabs balances sub-second responsiveness, custom vocal identity, and predictable cost structures.

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Pros and Cons of ElevenLabs Conversational AI

Evaluating ElevenLabs Conversational AI requires weighing its rapid development velocity and acoustic fidelity against commercial unit economics and architectural lock-in:

Pros

• Industry-Leading Voice Realism: Unrivaled vocal cadence, emotional expressiveness, and stability across 10,000+ synthetic and cloned voices spanning 70+ languages.
• Managed Turn-Taking and Interruption (Barge-In): Eliminates the need to construct, calibrate, and maintain complex WebRTC media servers, audio workers, or acoustic VAD pipelines.
• Robust Client and Server Tooling: Clean separation between secure server-side webhooks for private business logic and client tools for real-time frontend UI updates.
• Zero-Secret Client Architecture: Built-in ephemeral signed URL authentication (15-minute validity window) prevents private API keys from leaking into client-side browser bundles.
• Omnichannel Extensibility: Native support for WebRTC, WebSocket, and SIP telephony connections, with options for both managed LLMs and Bring-Your-Own-LLM (BYO-LLM) configurations.

Cons

• Higher Unit Cost at Scale: Conversational runtime costs ($0.08–$0.10/min) exceed the raw infrastructure cost of assembling commodity open-source components for high-volume telemarketing.
• No Air-Gapped or Fully Offline Operation: Cloud-managed infrastructure requires constant internet egress, preventing deployment in strictly isolated on-premise environments.
• Tier Feature Gating: Critical capabilities such as Professional Voice Cloning (Creator+), team workspace seats (Scale+), and custom LLM/telephony endpoints require higher-tier plans.
• Secondary Cost Dependencies: External model token fees for BYO-LLM configurations and SIP/Twilio trunking charges for telephony are billed separately outside ElevenLabs minutes.
• Runtime Lock-In: Dialogue state handling, turn detection, and synthesis models are tightly integrated into the ElevenLabs runtime, making provider migration non-trivial.

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Step-by-Step Implementation: Building an Interactive Voice Agent

Let us construct an interactive web application using the official @elevenlabs/react SDK and a secure Node.js backend. In this implementation, the agent acts as an interactive assistant capable of controlling the application interface via client tools.

Step 1: Agent Configuration and Client Tool Schema

In the ElevenLabs Agent Console (or via the REST API), provision an agent and attach a client tool. As defined in the ElevenLabs Client Tools Documentation, a client tool requires a descriptive name, instructions for the LLM, and a parameter schema.

Save the following configuration as client_tool.json:

{
  "type": "client",
  "name": "updateServiceSelection",
  "description": "Updates the highlighted service card on screen when the user chooses an option.",
  "expects_response": true,
  "parameters": {
    "type": "object",
    "properties": {
      "serviceId": {
        "type": "string",
        "description": "Service ID: 'routine-checkup' or 'cleaning'."
      }
    },
    "required": ["serviceId"]
  }
}

When expects_response is set to true, the conversational agent pauses and awaits confirmation from the React frontend before generating its spoken reply.

Step 2: Generating Ephemeral Signed URLs on the Backend

A common architectural vulnerability in client-side voice applications is exposing private API keys within browser bundles. According to the ElevenLabs Authentication Guide, client applications must authenticate using ephemeral signed URLs generated by your backend server.

Signed URLs expire 15 minutes after creation. While an active voice session can continue beyond 15 minutes once established, the WebSocket handshake must initiate within that initial window.

Install the required backend dependencies:

npm install express dotenv cors
npm install -D typescript ts-node @types/express @types/node @types/cors

Below is a secure Node.js Express endpoint utilizing the official @elevenlabs/elevenlabs-js client repository pattern (GitHub: elevenlabs-js) or native fetch, with environment configuration and CORS support enabled:

// server.ts - Secure backend token provider
import "dotenv/config";
import express, { Request, Response } from "express";
import cors from "cors";

const app = express();
const PORT = process.env.PORT || 3001;

// Enable CORS for cross-origin requests from frontend dev servers
app.use(cors());

const ELEVENLABS_API_KEY = process.env.ELEVENLABS_API_KEY;
const AGENT_ID = process.env.ELEVENLABS_AGENT_ID;

if (!ELEVENLABS_API_KEY || !AGENT_ID) {
  throw new Error("Missing required ElevenLabs environment variables.");
}

app.get("/api/get-signed-url", async (req: Request, res: Response) => {
  try {
    const targetUrl = new URL("https://api.elevenlabs.io/v1/convai/conversation/get-signed-url");
    targetUrl.searchParams.set("agent_id", AGENT_ID);

    const response = await fetch(targetUrl.toString(), {
      method: "GET",
      headers: { "xi-api-key": ELEVENLABS_API_KEY },
    });

    if (!response.ok) {
      const errorText = await response.text();
      return res.status(response.status).json({ error: errorText });
    }

    const data = await response.json();
    res.json({ signedUrl: data.signed_url });
  } catch (error) {
    res.status(500).json({ error: "Failed to generate session token." });
  }
});

app.listen(PORT, () => console.log(`Token server on port ${PORT}`));

Step 3: Frontend Integration with React and Client Tools

Install the official React SDK package:

npm install @elevenlabs/react

Per the ElevenLabs React SDK Reference, @elevenlabs/react re-exports underlying client utilities and provides fine-grained hooks to prevent excessive re-renders. All conversational hooks must reside within a <ConversationProvider> ancestor.

Per MDN Web Docs on MediaDevices.getUserMedia(), explicitly requesting user media prior to initializing the connection prevents browser autoplay blocks and unhandled stream errors.

Here is the interactive voice component:

// VoiceBookingAssistant.tsx
import React, { useState } from "react";
import {
  ConversationProvider,
  useConversationControls,
  useConversationStatus,
  useConversationMode,
  useConversationClientTool,
} from "@elevenlabs/react";

function VoiceWidget() {
  const { startSession, endSession } = useConversationControls();
  const { status } = useConversationStatus();
  const { isSpeaking, isListening } = useConversationMode();

  const [selectedService, setSelectedService] = useState<string>("none");
  const [isConnecting, setIsConnecting] = useState<boolean>(false);

  useConversationClientTool(
    "updateServiceSelection",
    async (params: { serviceId: string }) => {
      setSelectedService(params.serviceId);
      return `Highlighted service ${params.serviceId} on screen.`;
    }
  );

  const handleStartCall = async () => {
    try {
      setIsConnecting(true);
      await navigator.mediaDevices.getUserMedia({ audio: true });

      const response = await fetch("/api/get-signed-url");
      const { signedUrl } = await response.json();

      await startSession({ signedUrl });
    } catch (err) {
      console.error("Connection failure:", err);
    } finally {
      setIsConnecting(false);
    }
  };

  return (
    <div style={{ maxWidth: 440, padding: 24, background: "#1e293b", borderRadius: 12, color: "#fff" }}>
      <p>Status: <strong>{status}</strong> | Mode: <strong>{isSpeaking ? "Speaking" : isListening ? "Listening" : "Idle"}</strong></p>
      <p style={{ margin: "16px 0", padding: 12, background: "#0f172a", borderRadius: 8 }}>
        Selected Service: <strong>{selectedService}</strong>
      </p>
      {status === "connected" ? (
        <button onClick={endSession} style={{ width: "100%", padding: 12, background: "#dc2626", color: "#fff", border: "none", borderRadius: 8 }}>
          End Conversation
        </button>
      ) : (
        <button onClick={handleStartCall} disabled={isConnecting} style={{ width: "100%", padding: 12, background: "#16a34a", color: "#fff", border: "none", borderRadius: 8 }}>
          {isConnecting ? "Connecting..." : "Start Voice Intake"}
        </button>
      )}
    </div>
  );
}

export default function App() {
  return (
    <ConversationProvider serverLocation="us">
      <VoiceWidget />
    </ConversationProvider>
  );
}

Development Proxy Configuration

In VoiceBookingAssistant.tsx, the component fetches the ephemeral token using a relative path (fetch("/api/get-signed-url")). When running your React frontend development server (e.g., Vite on port 5173 or Create React App on port 3000) while server.ts listens on port 3001, configure a local development proxy so requests route seamlessly to Express without hardcoding local hosts:

If you are using Vite, add a proxy rule inside vite.config.ts:

// vite.config.ts
import { defineConfig } from "vite";
import react from "@vitejs/plugin-react";

export default defineConfig({
  plugins: [react()],
  server: {
    proxy: {
      "/api": {
        target: "http://localhost:3001",
        changeOrigin: true,
      },
    },
  },
});

If you are using Create React App, add a proxy field to your root package.json:

{
  "proxy": "http://localhost:3001"
}

Because CORS is also enabled on server.ts, you can alternatively target http://localhost:3001/api/get-signed-url directly or assign the URL via an environment variable (process.env.REACT_APP_API_URL or import.meta.env.VITE_API_URL).

The useConversationClientTool hook binds directly to the component lifecycle. When the agent recognizes that the caller requested a procedure, it invokes updateServiceSelection over the open socket, immediately updating selectedService in React state while voice streaming continues uninterrupted.

If you are deploying an automated appointment receptionist for healthcare or local services, our dental-clinic-landing template provides a polished, conversion-ready front-of-house layout to host the interactive voice widget.

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Production Pitfalls and Architectural Limits

Deploying voice AI to real-world end users exposes edge cases absent in text chat:

1. Acoustic Feedback and Missing Hardware AEC

Desktop users frequently converse through open speakers. If the microphone picks up the agent's voice, the system detects an interruption and halts playback. While browsers provide Acoustic Echo Cancellation (AEC) via getUserMedia({ audio: { echoCancellation: true } }), low-end hardware can still leak audio. Encourage headphone usage in your onboarding UI.

2. Blocking Tool Latency vs. Conversational Flow

When an agent calls a webhook requiring two seconds to query a database, dead air results. To prevent awkward pauses:

• Enable ambient tool sounds or pre-tool filler phrases ("Let me look that up...") in agent settings.
• Avoid multi-step synchronous webhooks during a single turn; delegate long tasks to background jobs and confirm receipt verbally.

3. Session Duration Limits and Concurrency Quotas

Sessions enforce safety boundaries: duration defaults to 10 minutes (600 seconds), but is configurable up to 7,200 seconds (120 minutes) via max_duration_seconds under the agent's advanced call limits or session configuration. Ensure your tier's concurrency limits accommodate peak traffic to prevent dropped connections.

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ElevenLabs Conversational AI Pricing and Plan Breakdown

(Pricing and plan specifications verified as of October 2026)

Unlike standard Text-to-Speech synthesis billed purely on character consumption, ElevenLabs Conversational AI evaluates usage through active dialogue runtime. Each plan tier bundles a designated pool of conversational call minutes and enforces maximum concurrent call limits:

Plan Tier	Monthly Billing	Annual Billing	Bundled Call Minutes	Concurrency Limit	Primary Unlocks & Key Capabilities
Starter	$6 / mo	$5.00 / mo ($60/yr)	75 min	6 calls	Commercial license, Instant Voice Cloning, entry-level API access
Creator	$22 / mo	$18.33 / mo ($220/yr)	275 min	10 calls	Professional Voice Cloning (PVC), higher audio quality
Pro	$99 / mo	$82.50 / mo ($990/yr)	1,238 min	20 calls	Studio-grade output (44.1kHz PCM), advanced analytics, custom LLM & telephony support
Scale	$299 / mo	$249.00 / mo ($2,988/yr)	3,738 min	30 calls	Team workspace seats (3 seats included), dedicated volume onboarding
Business	$990 / mo	$825.00 / mo ($9,900/yr)	12,375 min	40 calls	10 workspace seats, enterprise concurrency headroom, lowest effective rate

Standard conversational runtime generally bills around $0.08 to $0.10 per minute, with additional overage minutes billed automatically if you exceed your monthly quota.

Feature Gating Across Subscription Tiers

ElevenLabs gates several critical capabilities by tier:

• Instant vs. Professional Voice Cloning: Starter grants Instant Voice Cloning (rapid cloning from short audio samples). Professional Voice Cloning (PVC)—essential for hyper-realistic brand voice actors and emotional consistency—requires Creator or higher.
• Team Workspace Collaboration: Multi-seat team workspaces are locked to Scale and Business tiers (Scale includes 3 seats; Business includes 10 seats). Starter and Creator accounts are restricted to individual single-seat credentials.
• Custom LLM and Telephony Endpoints: Integrating Bring-Your-Own-LLM (BYO-LLM) endpoints (such as private OpenAI, Anthropic, or open-source vLLM backends) and custom SIP/telephony configurations is fully supported on Pro, Scale, and Business tiers, ensuring low-latency protocol routing and custom enterprise telephony hooks. For enterprise-grade HIPAA compliance (BAA), custom SLAs, and custom concurrency allocations beyond 40 streams, ElevenLabs offers custom Enterprise contracts.

Workload-to-Plan Buying Guidance

Selecting the proper tier depends directly on your daily call volume and concurrency profile:

• Prototyping & Developer Testing (Starter Plan — $6/mo): Ideal for sandbox experiments, integration testing, and configuring client tools. 75 bundled minutes and a 6-call concurrency limit accommodate extensive developer iteration without significant financial commitment.
• Low-Volume Specialist / Creator Assistant (Creator Plan — $22/mo): Best suited for portfolio sites, niche consultants, or internal tools handling infrequent calls (up to ~9 minutes of dialogue per day) where having a hyper-realistic cloned voice via PVC is essential.
• Dental Clinic / Local Reception (~30 calls/day): Consider a dental clinic or medical office handling 30 patient intake or booking calls each day. Assuming an average conversation duration of 3 minutes, the clinic consumes roughly 90 minutes daily, or ~2,700 minutes per month:
  • The Creator Plan (275 min) would exhaust its quota within the first 3 days.
  • The Pro Plan ($99/mo) provides 1,238 minutes and 20 concurrent lines—covering roughly 14 calls per day. The remaining 1,462 minutes would incur usage overages ($117–$146 at overage rates), bringing total monthly spend to ~$216–$245.
  • The Scale Plan ($299/mo) provides 3,738 minutes and 30 concurrent lines. This completely absorbs the 2,700 monthly minutes with 1,038 surplus minutes of headroom for holiday spikes, while unlocking team workspace seats so multiple clinic staff members can review call analytics simultaneously. For consistent 30 calls/day operations, Scale is the recommended production tier, while Pro serves as an acceptable baseline if initial call volume ramps up gradually.
• Multi-Location & Scaled Inbound Operations (Business Plan — $990/mo): Designed for high-volume customer support or multi-branch clinics handling 100+ calls daily (~10,000+ monthly minutes) and requiring up to 40 concurrent lines with multi-seat collaboration.

Secondary Cost Dependencies

When budgeting for production deployments, account for external infrastructure costs that lie outside ElevenLabs subscription billing:

• Bring-Your-Own-LLM (BYO-LLM) Token Costs: If you configure your agent to route reasoning through an external model endpoint (e.g., Anthropic Claude, OpenAI GPT-4o, or self-hosted models) rather than ElevenLabs' native hosted LLMs, you are directly billed for prompt and completion token spend by the external model provider in addition to ElevenLabs conversational minute fees.
• Telephony & SIP Carrier Fees: Deploying an agent to handle live phone numbers (inbound or outbound PSTN/telephony) requires connecting via SIP trunking or providers such as Twilio. Telephony carrier fees—including local or toll-free number rentals, inbound/outbound per-minute carrier rates (typically ~$0.004–$0.014/min for standard SIP termination), and regulatory telecommunication fees—are billed independently by your telephony carrier.

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Who Should Skip ElevenLabs Conversational AI

While ElevenLabs provides a leading managed voice platform, certain architectures warrant alternative tools:

• Air-Gapped or Offline Deployments: Environments requiring zero internet egress cannot use cloud runtimes. Deploy local pipelines instead (e.g., Whisper cpp with Ollama and Piper TTS).
• High-Volume Commodity Telemarketing: Workloads requiring hundreds of thousands of daily outbound calls with unit costs below $0.02 per minute will find managed voice pricing prohibitive.
• Teams with In-House WebRTC Infrastructure: Organizations operating scaled LiveKit or Janus SFU clusters may find a managed platform duplicates existing media investments without added flexibility.

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Worth a look: If your team needs human-grade vocal inflection, seamless interruption handling, and rapid frontend integration, ElevenLabs provides a robust conversational stack; skip it if your system requires fully offline inference or strict on-premise data localization.

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What to Do Next

1. Provision an Agent: Navigate to the ElevenLabs dashboard, select a conversational voice persona, and configure your system prompt and turn-taking sensitivity.
2. Implement Backend Signing: Add an authenticated /api/get-signed-url endpoint to your server to protect your private API key from browser inspection.
3. Connect Your Frontend: Install @elevenlabs/react, wrap your layout with <ConversationProvider>, and use useConversationControls() to establish audio streaming.
4. Wire Up Client Tools: Identify two or three interactive UI states in your frontend (such as cart updates or modal triggers) and register them via useConversationClientTool.
5. Run Acoustic Evals: Test the conversational agent across noisy environments and varying speaker hardware to fine-tune interruption sensitivity before launching to production.