Building a voice-enabled AI agent is no longer a futuristic experiment – it’s a core capability for any company aiming to offer real-time, conversational experiences through their products.
With the release of OpenAI’s AgentKit and the growing maturity of voice infrastructure platforms like FreJun Teler, developers can now design, deploy, and scale human-like voice agents that operate in real-world telephony environments – without needing to build complex call-handling systems from scratch.
In this step-by-step guide, we’ll explore how to integrate LLMs, STT, TTS, and tool calling into a seamless voice AI pipeline.
We’ll also cover where Teler fits into this architecture – as the transport layer that makes your AI agent speak and listen in real time.
What Are Voice-Enabled AI Agents and How Do They Work?
Voice-enabled AI agents are applications capable of real-time, two-way speech interaction. Global device forecasts estimate approximately 8.4 billion voice assistants in use by the end of 2025, up from 4.2 billion in 2020.
At their core, they combine four essential components:
| Component | Function | Example Tools |
| Speech-to-Text (STT) | Converts user speech to text for AI processing. | Whisper, Deepgram, Google STT |
| Language Model (LLM) | Processes text input, understands intent, and generates responses. | GPT-4, Claude, Llama 3 |
| Text-to-Speech (TTS) | Converts the model’s text output back to speech. | ElevenLabs, Play.ht, Azure TTS |
| Voice Transport Layer | Handles real-time audio streaming, telephony routing, and latency control. | FreJun Teler |
In short, the process looks like this:
- User speaks – audio captured and streamed in real time.
- Speech converted – STT transcribes to text.
- Text sent to AI – LLM processes and generates the next response.
- Response converted – TTS transforms text into natural speech.
- Speech streamed back – via Teler or another real-time voice interface.
This loop repeats continuously within milliseconds – forming a natural conversation between human and machine.
Why Use OpenAI’s AgentKit for Building AI Agents?
OpenAI’s AgentKit makes it easier to transform any LLM into an interactive, goal-driven agent that can reason, call tools, and manage state across conversations.
Key Benefits of AgentKit
- Agent Memory and Context Management: Automatically maintains conversational history and parameters.
- Tool Integration (via MCP): Enables the AI to call APIs, databases, or internal systems dynamically.
- Secure Environment: AgentKit allows you to safely define tool access and permissions.
- Scalable Foundation: Works with OpenAI models but also supports general agent frameworks.
AgentKit Architecture in Short
AgentKit essentially acts as the “control center” of your AI.
It orchestrates inputs, outputs, and tool calls – making it the perfect backbone for building intelligent, autonomous voice assistants.
When paired with a voice infrastructure like Teler, it enables a fully operational real-time AI assistant that can:
- Listen and respond instantly.
- Retrieve data from APIs or CRMs.
- Automate repetitive voice workflows.
Discover how developers integrate Teler and AgentKit to craft natural, low-latency voice agents that replicate real human conversation flow.
What Is the Basic Architecture of a Voice-Enabled AI System?
A robust voice AI pipeline integrates multiple components in a structured flow.
Here’s the simplified architecture to understand before implementation:
User Speech
↓
Speech-to-Text (STT)
↓
LLM / AgentKit (Reasoning + Tool Calls)
↓
Text-to-Speech (TTS)
↓
FreJun Teler (Voice Transport + Call Management)
↓
User (Audio Playback)
Let’s break this down step by step.
Stage 1: Capture Speech Input
Use an STT system like OpenAI Whisper API or Deepgram.
These systems transcribe speech into text with low latency. You can stream audio input using Teler’s media streaming or WebRTC setup.
Stage 2: Process with the LLM
Feed transcribed text into your LLM through AgentKit.
AgentKit handles the following:
- Session context and memory retention.
- Calling external tools or APIs using MCP (Model Context Protocol).
- Structured output formatting for response generation.
Stage 3: Generate Speech Output
Use any Text-to-Speech (TTS) engine – ElevenLabs or Azure TTS – to convert the response text into natural voice.
Stage 4: Deliver Over Voice
Finally, use FreJun Teler to stream the synthesized voice back to the user through real-time telephony or VoIP.
Each stage can be independently optimized or swapped, depending on your preferred stack.
How to Prepare Your Development Environment for Voice AI?
Before you begin building, ensure your environment is ready for streaming audio and API orchestration.
Prerequisites
- Node.js or Python installed
- OpenAI API key
- Access to Teler API (for telephony and streaming)
- TTS and STT provider credentials
Recommended Setup
Create a project structure like this:
voice-agent/
├── server.js (or main.py)
├── config/
│ ├── openai.js
│ ├── teler.js
│ ├── tts.js
│ └── stt.js
├── utils/
│ ├── streamHandler.js
│ └── audioBuffer.js
This modular layout allows you to handle audio streams, AI responses, and telephony logic separately – making the build scalable.
How to Connect AgentKit and Your LLM?
Let’s start with the brain of the system – your LLM agent.
Step 1: Initialize AgentKit
You can use the official OpenAI SDK to create a simple agent loop.
from openai import OpenAI
client = OpenAI()
agent = client.beta.agents.create(
name=”Voice Assistant”,
instructions=”You are a helpful, real-time voice assistant.”,
tools=[
{“type”: “mcp”, “name”: “weather_api”, “url”: “https://api.weatherapi.com/v1/current.json”}
]
)
This example defines an agent with tool-calling ability via MCP (Model Context Protocol).
Step 2: Handle Input and Output
Your main loop listens for incoming user text (from STT) and sends it to the agent.
response = client.beta.agents.sessions.respond(
agent_id=agent.id,
session_id=session_id,
input=”What’s the weather in San Francisco?”
)
print(response.output)
This response will later be fed to your TTS system to produce audio.
Step 3: Add Memory or Context
AgentKit also supports persistent sessions.
This ensures the AI remembers prior exchanges, maintaining continuity in multi-turn conversations – crucial for real-time assistants.
Learn how Teler’s voice infrastructure and OpenAI’s AgentKit are redefining real-time AI voice automation for next-gen digital products.
How Do You Handle Real-Time Audio Streaming?
Handling real-time audio efficiently determines how “human” your AI will sound.
For production systems, the biggest challenge isn’t just generating accurate speech but doing so within milliseconds.
Key Concepts to Remember
- Bidirectional Streaming: Simultaneous input (speech) and output (AI response).
- Low Latency: Target total round-trip latency below 400ms for natural flow.
- Audio Buffers: Process small audio chunks (100–300ms).
- WebSocket or gRPC: For continuous, duplex audio data exchange.
Typical Flow
- Client starts a call via Teler’s telephony SDK or WebRTC interface.
- Audio stream sent to STT engine – transcribed in real-time.
- Transcription text fed into AgentKit for processing.
- AgentKit response – sent to TTS engine.
- Generated speech streamed back to caller through Teler’s API.
By combining Teler’s low-latency infrastructure with streaming APIs for STT/TTS, you achieve near-instant conversational response times.
How to Integrate TTS and STT Systems for Better Accuracy?
Choosing the right TTS and STT engines impacts both accuracy and realism.
Speech-to-Text (STT) Selection Criteria
- Accuracy on diverse accents and noise.
- Streaming API support.
- Confidence scores in transcripts.
- Low response latency.
Example Choices:
OpenAI Whisper, Google Speech API, Deepgram, AssemblyAI.
Text-to-Speech (TTS) Selection Criteria
- Voice customization (gender, tone, emotion).
- Speed of generation.
- SSML support for controlling pauses or emphasis.
- Streaming audio output.
Example Choices:
ElevenLabs, Azure Cognitive Services, Play.ht, Coqui.
You can wrap these APIs with WebSocket streams, enabling continuous audio flow – instead of waiting for full sentences before playback.
How to Manage Conversational Context and State?
Without proper context handling, even the best AI will sound robotic or disconnected.
Techniques to Maintain Context
- Use AgentKit’s session persistence to remember user history.
- Maintain short-term state (for current call) in memory.
- Store long-term context (user preferences, CRM data) in external databases.
Example
User: Book me a meeting with John tomorrow.
AI: Sure. Which John do you mean?
User: John from marketing.
AI: Got it. Scheduling with John from marketing tomorrow at 10 AM.
This requires maintaining references across turns – something AgentKit handles through session tracking.
What Role Does FreJun Teler Play in a Voice AI Stack?
Before you can deliver a true real-time AI assistant, you need a reliable way to move audio in and out of your system – across phone networks, VoIP, or web calls. That’s where FreJun Teler comes in.
What is Teler?
FreJun Teler is a global voice infrastructure API that bridges your AI backend with telephony and VoIP networks. It enables two-way streaming audio between users and AI models with minimal delay.
Unlike typical APIs that handle only call initiation or recording, Teler manages media streaming, signaling, and call control – allowing your LLM or AI agent to “speak” and “listen” like a human.
Core Capabilities
- Real-Time Media Streaming: Sub-300ms latency for live speech capture and playback.
- LLM and AI Agnostic: Works with any model or agent framework, including OpenAI AgentKit, Anthropic, or self-hosted models.
- Bidirectional Audio Pipeline: Stream voice data both ways – from caller to AI and back – in near real-time.
- Cloud Telephony Compatibility: Supports SIP Trunking, PSTN, and VoIP networks globally.
- Developer-First SDKs: Ready-to-integrate Node.js, Python, and REST SDKs for rapid prototyping.
Teler effectively acts as the voice interface for your AI – you bring the intelligence; Teler makes it talk.
How Does Teler Handle Real-Time Audio and Network Latency?
Real-time speech interactions demand tight latency control across the entire chain – capture – process – response – playback.
Let’s look at how Teler optimizes this process.
Low-Latency Design
Teler employs a packet-based streaming model that captures and transmits audio in micro-chunks (typically <200ms).
These are processed in parallel while subsequent packets continue streaming – avoiding blocking delays.
| Operation | Typical Latency |
| Audio capture and encoding | 50–80 ms |
| Network transmission | 100–150 ms |
| AI response + TTS synthesis | 100–200 ms |
| Playback start | <300 ms total |
This design keeps round-trip latency under the conversational threshold of 400ms, ensuring natural flow.
Adaptive Stream Buffering
Teler also dynamically adjusts stream buffers based on network conditions, preventing jitter or stuttering during high-traffic sessions.
Integration Advantage
You don’t have to manage RTP streams, sockets, or SIP signaling manually. Teler abstracts that layer through APIs like:
POST /calls
POST /stream/start
POST /stream/stop
This means your AI backend just plugs into Teler’s endpoints, not into raw telephony complexity.
How to Connect Teler with AgentKit and Your AI Pipeline
Now let’s bring everything together – the LLM (via AgentKit), STT, TTS, and Teler – into one unified workflow.
Step 1: Initiate a Call via Teler
Create a call session using Teler’s REST API:
POST https://api.frejun.ai/v1/call
{
“phone”: “+14152007986”,
“app_id”: “your_app_id”,
“webhook_url”: “https://your-backend/handle-audio”
}
Once the call starts, Teler begins streaming inbound audio to your webhook in real-time.
Step 2: Stream Audio to STT Engine
At your webhook endpoint, capture incoming media packets:
@app.route(‘/handle-audio’, methods=[‘POST’])
def handle_audio():
audio_chunk = request.data
stt_text = stt_stream.process(audio_chunk)
send_to_agent(stt_text)
Step 3: Send Transcription to AgentKit
Now forward the STT output to your AgentKit session:
response = client.beta.agents.sessions.respond(
agent_id=agent.id,
session_id=session_id,
input=stt_text
)
Step 4: Convert Response to Speech
Take the agent output and send it to your TTS service:
audio_response = tts_stream.synthesize(response.output)
Step 5: Stream Back via Teler
Finally, send the audio stream back to the same call:
teler.stream.play(audio_response)
This closes the conversational loop – Teler routes the response back to the user instantly, maintaining a continuous live interaction.
How Does Teler Enable Enterprise-Grade Voice AI?
For engineering teams and founders building production-scale systems, reliability and observability matter as much as speed.
Teler’s Enterprise Features
- Scalable Voice Channels: Handle thousands of concurrent calls globally.
- Geo-Distributed Infrastructure: Edge nodes positioned across regions for minimum latency.
- Guaranteed Uptime: SLA-backed reliability for mission-critical deployments.
- Secure Audio Handling: Encrypted voice streams and webhook signing.
- Detailed Logs & Metrics: Monitor per-call events, duration, and quality stats via API or dashboard.
Why This Matters
When integrating with AgentKit or similar LLM frameworks, Teler eliminates the operational burden of telephony, letting your engineers focus on optimizing the intelligence and flow of your voice AI – not the infrastructure. According to a 2023 Gartner-cited study, about 80% of organisations are using or planning to deploy AI-powered bots for customer service by 2025.
How to Design a Scalable Voice AI Architecture (Example Setup)
Here’s a typical reference architecture for deploying a large-scale real-time voice AI using Teler + OpenAI AgentKit:
┌────────────────────────────┐
│ User Caller │
└────────────┬───────────────┘
│
(Telephony / VoIP)
│
┌────────▼────────┐
│ FreJun Teler │ ←- Call Control + Media Streaming
└────────┬────────┘
│
┌─────────▼──────────┐
│ AI Backend API │
│ (Webhooks, STT/TTS)│
└─────────┬──────────┘
│
┌──────────────▼────────────────┐
│ OpenAI AgentKit / LLM Logic │
│ (Context, Reasoning, Tools) │
└──────────────┬────────────────┘
│
┌────────▼────────┐
│ External APIs │
│ (CRM, DB, RAG) │
└─────────────────┘
This structure ensures clear separation of responsibilities – Teler handles communication, AgentKit manages intelligence, and external systems power your business logic.
What Are Common Use Cases for Teler + AgentKit Integration?
When combined, Teler and AgentKit can power a wide range of real-world AI applications:
| Use Case | Description | Example |
| AI Receptionists | Automatically answer and route inbound calls using natural language understanding. | 24/7 customer support bot |
| Outbound Lead Qualification | AI initiates outbound calls and qualifies leads based on script logic. | SaaS sales automation |
| Follow-Up and Feedback Calls | Automatically collect NPS or post-purchase feedback. | E-commerce |
| Appointment Scheduling | AI agent books or reschedules appointments through calendar APIs. | Healthcare, services |
| Voice-Driven CRMs | Voice-based access to CRM or ERP data using LLM queries. | Field teams |
Each of these cases follows the same core pipeline – voice capture – AI reasoning – response synthesis – voice playback.
How to Optimize and Monitor Your Voice AI Performance?
Even the most advanced pipelines need fine-tuning.
Here are a few strategies to optimize latency, accuracy, and scalability:
Latency Optimization
- Use streaming STT/TTS APIs instead of batch requests.
- Deploy your AI backend close to Teler’s edge nodes.
- Process audio in small chunks (under 200ms).
Accuracy Optimization
- Apply domain-specific fine-tuning for your LLM (e.g., customer service tone).
- Use acoustic enhancement or noise suppression for better STT results.
- Test multiple TTS voices for clarity and realism.
Scalability Optimization
- Run stateless agent sessions that fetch context on demand.
- Cache frequently accessed tools or knowledge sources.
- Use Teler’s load balancing and webhooks for concurrent call management.
What’s the Future of Voice-Enabled AI Agents?
Voice AI is entering a new phase – moving from scripted IVRs to autonomous, context-aware, real-time conversations.
With frameworks like OpenAI’s AgentKit, developers can design agents that reason, remember, and act.
With platforms like Teler, they can deploy these agents at global telephony scale – turning any product into a real-time voice assistant that connects over phone, browser, or app.
As LLMs evolve with multimodal and low-latency streaming, and as Teler continues to advance its voice infrastructure, the barrier between human and digital conversation will keep narrowing.
Ready to Build Your Own Real-Time Voice Agent?
Here’s a quick recap of the steps:
| Step | Action | Tool/Platform |
| 1 | Set up your development environment | Node.js / Python |
| 2 | Connect your STT and TTS services | Whisper, ElevenLabs |
| 3 | Build your LLM logic via AgentKit | OpenAI AgentKit |
| 4 | Integrate FreJun Teler for real-time streaming | Teler API |
| 5 | Test, optimize, and scale | Observability tools |
By combining AgentKit’s intelligence with Teler’s voice infrastructure, you can build a production-grade, voice-enabled AI system that responds instantly, understands context, and sounds remarkably natural.
Final Thoughts
If you’re a founder, product manager, or engineering lead, the path to building voice-enabled AI agents no longer requires piecing together complex telephony, latency tuning, or streaming pipelines from scratch. With Teler, your real-time voice infrastructure is already optimized for low-latency, bidirectional audio, and scalable session handling. Pair it with OpenAI’s AgentKit for the cognitive layer, and you get a seamless bridge between voice, logic, and real-time intelligence. This integration allows your teams to focus on designing the user experience, not debugging the infrastructure.
Start Building Today – integrate Teler into your AI stack and turn your ideas into production-grade, real-time voice agents.
Schedule a Demo to explore how Teler can power your next intelligent product.
FAQs –
- What is FreJun Teler?
Teler is a global voice infrastructure API enabling low-latency, real-time voice conversations for AI agents and applications. - Can I use any LLM with Teler?
Yes. Teler is model-agnostic, meaning you can connect OpenAI, Anthropic, or any custom-trained LLM seamlessly. - Does Teler support real-time streaming?
Absolutely. Teler handles bi-directional audio streaming to ensure zero-lag, human-like conversational experiences. - Is Teler compatible with AgentKit?
Yes. Teler connects perfectly with AgentKit for building voice-based AI agents capable of reasoning and real-time communication. - Do I need separate telephony infrastructure?
No. Teler manages all cloud telephony and VoIP integrations, removing your need for complex telecom setup. - How is latency managed in Teler?
Teler uses optimized streaming protocols and distributed servers to minimize round-trip delays during voice interactions. - What are common use cases?
AI receptionists, lead qualification agents, support assistants, and outbound voice campaigns that operate fully autonomously. - Can I customize the voice output?
Yes. You can integrate any TTS engine to control voice style, tone, and language dynamically. - Is developer setup complex?
No. With SDKs and REST APIs, developers can implement Teler within minutes for real-time AI integration. - Does Teler ensure data security?
Yes. Teler includes encryption, secure transport protocols, and enterprise-grade reliability for confidential data exchange.