Real-time voice agents are redefining how applications interact through speech, but building one that feels natural, fast, and scalable remains a challenge. This blog unpacks the complete technical flow of integrating FreJun Teler with AgentKit using the MCP server, the backbone for low-latency, bidirectional communication between your LLM, STT, and TTS layers.

Whether you’re a founder, product manager, or engineering lead, you’ll learn how to connect these components efficiently and deploy a production-grade, real-time voice AI system. By the end, you’ll see how Teler simplifies what used to take months into a seamless integration process.

What Makes Real-Time Voice Agents the Next Big Leap in AI Communication?

User conversations are becoming the new interface layer. Traditional chatbots or web forms are being replaced by real-time voice agents that can talk, listen, and act – instantly. Enterprises implementing AI agents in contact centres achieved up to 50% reduction in cost per call.

A real-time voice agent combines multiple components into a single operational flow:

Speech-to-Text (STT): Converts live user speech into text.
Large Language Model (LLM): Understands context, intent, and next action.
Model Context Protocol (MCP): Allows the agent to execute live tool calls or API actions.
Text-to-Speech (TTS): Turns the AI’s response into natural speech.
Transport Layer: Streams audio back to the user with minimal latency.

However, the real challenge lies in synchronizing all these systems – each must communicate in milliseconds to maintain a natural, human-like rhythm. That’s where the MCP server integration and Teler realtime API become critical.

By using a consistent protocol and a low-latency voice layer, founders and product teams can deploy AI communication bridges that function smoothly across phone lines, browsers, or mobile apps.

How Does the MCP Server Fit into the Voice Agent Ecosystem?

To understand how the MCP server works, think of it as the “bridge” that connects your voice agent’s intelligence to real-world functionality.

The Role of MCP

MCP (Model Context Protocol) provides a standardized way for an agent to call external tools or APIs.
It defines how the agent requests actions (for example, checking a user’s account balance) and how responses are structured and returned.
By decoupling logic from communication, MCP ensures that your AgentKit and Teler setup can access multiple backend systems without breaking context.

In a real-time voice agent, MCP operates during a conversation, not after it.
For instance:

A caller asks, “Can you schedule a meeting for tomorrow?”
The agent processes this through AgentKit, identifies it as a calendar action, and calls the MCP endpoint /calendar/schedule.
MCP triggers your internal API.
The response returns structured data (e.g., confirmation time).
The agent formulates a voice reply instantly.

This event loop takes place within seconds, enabling near-natural conversations between users and your AI-driven system.

MCP’s Technical Benefits

Modular Tool Access: Any service can be exposed as a tool via a simple JSON schema.
Low Latency: MCP supports streaming updates to reduce waiting time.
Secure Gateway: It adds isolation between your AI logic and sensitive internal APIs.
Reusability: Tools defined once can be reused across multiple voice or chat interfaces.

Overall, MCP ensures your voice agent isn’t limited by its LLM – it can execute live actions just like a human operator.

What Exactly Are AgentKit and the OpenAI AgentKit API?

AgentKit is a lightweight orchestration framework that brings structure to your AI agents. It provides APIs for dialogue state management, function execution, and memory handling. When combined with the OpenAI AgentKit API, it can manage multi-turn conversations and external calls efficiently.

In this setup, AgentKit acts as the core brain that coordinates between user input, LLM reasoning, and MCP-based actions.

Key Capabilities

Event Handling: Accepts transcript updates in real-time.
Tool Invocation: Calls MCP endpoints securely when an action is required.
State Management: Maintains contextual memory across sessions.
Multi-Model Compatibility: Works with OpenAI, Anthropic, Mistral, or any other LLM API.

Typical Workflow

Receive user text from the Speech-to-Text pipeline.
Parse and interpret user intent.
Decide whether to respond directly or call an MCP tool.
Generate a reply and forward it to the Text-to-Speech engine.

The OpenAI AgentKit API provides methods for function calling, event streaming, and structured responses. This design allows developers to add or remove modules – such as voice, vision, or memory – without rewriting core logic.

With AgentKit, the MCP server becomes more than a bridge; it’s a way to make the agent operational in production.

Curious why enterprises are rapidly adopting AI-powered voice systems? Discover real-world benefits and business shifts in our latest analysis.

Why Is Real-Time Audio Transport Crucial for Voice Agents?

Even the smartest logic fails if your user hears awkward delays or clipped speech. For that reason, real-time transport is the backbone of every conversational system.

Challenges in Voice Streaming

Round-Trip Latency: The time from user speech → AI processing → response playback must stay under 800ms for natural flow.
Packet Loss & Jitter: Inconsistent audio packets break continuity.
Interruptions: Users often “barge in” before the AI finishes speaking; handling this cleanly requires dynamic audio control.

To solve these issues, voice platforms rely on media streaming APIs that provide:

Persistent bidirectional audio channels (WebSocket, WebRTC, or SIP).
Adaptive buffering to handle network variability.
Real-time transcription and playback hooks.

Once integrated, these layers allow continuous audio exchange, where both user and agent can speak naturally.

In the second half of this blog, we’ll see how FreJun Teler provides this real-time infrastructure, connecting AgentKit and MCP seamlessly through its Teler realtime API.

How Do You Architect a System That Combines Teler, AgentKit, and the MCP Server?

Before diving into setup, it helps to visualize the architecture.

Component	Purpose	Example Technology
Teler (Transport)	Handles real-time call audio streaming, signaling, and playback	FreJun Teler Realtime API
STT Engine	Converts user speech to text	Whisper, AssemblyAI, or Google STT
AgentKit Core	Orchestrates logic, context, and tool calls	OpenAI AgentKit API
MCP Server	Executes external actions securely	Custom Node.js or FastAPI service
TTS Engine	Synthesizes natural speech from responses	ElevenLabs, Azure TTS, Polly

Workflow at a Glance

User speaks during a call initiated through Teler.
Audio stream is routed in real time to your STT engine.
Transcript events reach AgentKit, which interprets and decides on an action.
If required, AgentKit makes a tool call via MCP.
The MCP server performs the action (e.g., fetch data or trigger a workflow).
Response text is sent to TTS, then streamed back to Teler.
The user hears the agent’s spoken reply instantly.

Latency Control Points

STT latency (150–300ms typical)
MCP round-trip (100–200ms average)
TTS playback buffer (<300ms ideal)

By optimizing each component’s streaming interface, you can maintain total round-trip latency under 1 second, the threshold for fluid, natural speech interaction.

Explore how multimodal AI agents combine text, vision, and voice to create next-gen conversational systems beyond traditional chatbots.

How Do You Set Up and Configure the MCP Server for Real-Time Tool Calls?

Once your AgentKit session is operational and receiving live transcripts, the next step is to build your MCP server – the bridge that turns voice commands into real-world actions.

The Model Context Protocol (MCP) defines how an AI agent interacts with external tools. In this case, your MCP server will handle structured tool requests coming from AgentKit and respond with actionable data.

Step 1: Define MCP Tools

Each tool is a JSON schema that declares:

Tool name
Input parameters
Output format

Example:

{

“name”: “calendar.schedule”,

“description”: “Create a meeting in Google Calendar”,

“parameters”: {

“type”: “object”,

“properties”: {

“date”: { “type”: “string” },

“time”: { “type”: “string” },

“title”: { “type”: “string” }

“required”: [“date”, “time”, “title”]

}

This schema ensures that when your voice agent says “Schedule a meeting for tomorrow at 11 AM”, AgentKit can generate a valid structured call like:

{

“tool”: “calendar.schedule”,

“arguments”: {

“date”: “2025-11-01”,

“time”: “11:00”,

“title”: “Client Discussion”

}

Step 2: Create MCP Endpoints

In your backend (Node.js, Python, or Go), expose MCP endpoints to handle these calls.

Example (Node.js/Express):

app.post(“/mcp/tools/calendar.schedule”, async (req, res) => {

const { date, time, title } = req.body.arguments;

const response = await createGoogleCalendarEvent(date, time, title);

res.json({ result: `Meeting scheduled for ${date} at ${time}` });

});

Step 3: Register Tools with AgentKit

Once your tools are live, AgentKit needs to know where to find them:

agent.registerTools([

{ name: “calendar.schedule”, endpoint: “https://yourdomain.com/mcp/tools/calendar.schedule” }

]);

Now, every time the model identifies a tool call, it automatically reaches out to your MCP server.

Step 4: Enable Real-Time Streaming

MCP supports event-based responses for long-running or streaming actions.

For example, if a task takes several seconds (like generating a report), the MCP server can send intermediate updates via WebSocket to keep AgentKit informed.

How Does FreJun Teler Enable Real-Time Audio Response and Playback?

This is the point where FreJun Teler plays a crucial role.

While the MCP server handles data and logic, Teler’s realtime API manages the voice layer – turning structured responses into instant, natural speech on live calls. With the VoIP market already exceeding US$160 billion in 2025 and projected to grow further, platforms like Teler that deliver real-time voice transport are gaining compelling momentum.

Why Teler?

FreJun Teler is designed for developers building AI communication systems that require both high-quality voice transport and real-time API control.

It provides:

Low-latency bidirectional streaming (under 500ms round trip)
Dynamic playback control – pause, interrupt, or queue messages
Programmable call routing for inbound and outbound calls
Integration-ready WebSocket endpoints for AgentKit and MCP workflows

In simpler terms, Teler becomes the voice backbone between your LLM logic and the end-user’s ears.

Teler Playback Integration

Once the MCP server returns a response, you can trigger playback through the Teler API:

{

“action”: “play_tts”,

“call_id”: “call_123”,

“text”: “Meeting scheduled successfully for 11 AM tomorrow.”

}

Teler receives this payload and begins streaming synthesized audio back to the active call.
Under the hood, Teler:

Fetches or synthesizes the TTS audio.
Streams it back in real time.
Listens for barge-in events if the user interrupts.

Interrupt Handling

When users speak during playback, Teler pauses audio automatically and routes live speech chunks back to your STT pipeline.

This ensures continuous, conversational experience – something very few traditional telephony platforms can handle at scale.

Sign Up for Teler Today!

How to Connect All Components into a Unified Realtime Voice Flow

At this point, all individual systems – AgentKit, MCP, and Teler – are functional. Let’s connect them into a production-ready voice agent loop.

End-to-End Flow Diagram (Conceptual)

Stage	Description	Technology
1. Call Start	Teler handles SIP/WebRTC signaling and opens a bidirectional audio stream.	Teler realtime API
2. Speech Recognition	Audio sent to STT engine; transcript returned.	Whisper / AssemblyAI
3. Intent Processing	Transcript analyzed for actions.	AgentKit
4. Tool Call (Optional)	AgentKit invokes MCP tool if needed.	MCP Server
5. Response Generation	LLM forms structured response.	OpenAI / Anthropic
6. Speech Playback	Response text streamed back via Teler.	TTS + Teler
7. User Interruption	Teler handles barge-in and loops back.	Teler event handler

This real-time cycle repeats for every user message until the call ends.

What Are the Best Practices for MCP Server Integration and Latency Optimization?

Even small inefficiencies can degrade conversational quality. Below are performance and architectural best practices for real-world deployments.

Latency Optimization

Keep your MCP server in the same region as your STT and TTS providers.
Use WebSocket for streaming instead of plain HTTP POST for faster turnaround.
Enable connection pooling for AgentKit ↔ MCP communication.
Preload TTS models to reduce synthesis time.

Error Handling

Always return structured JSON errors from MCP tools.
Use retry queues for transient API failures.
Maintain session logs in AgentKit for debugging real-time issues.

Scaling and Load

Deploy MCP tools as stateless microservices.
Use message brokers (Redis, NATS) for event streaming at scale.
Teler supports auto-scaling for concurrent live calls via its realtime cluster API.

Observability

Log MCP request and response times.
Monitor call latency through Teler metrics API.
Use Prometheus or Grafana for latency visualization.

How Does FreJun Teler Outperform Other Telephony Platforms for AI Voice Agents?

Most calling APIs are designed for static IVR menus or pre-recorded prompts.
FreJun Teler, in contrast, was built from the ground up for AI-driven, real-time voice experiences.

Technical Differentiators of Teler

Feature	Traditional Cloud Telephony	FreJun Teler
Audio Transport	Linear RTP streams	Realtime bidirectional streaming (WebSocket/SIP hybrid)
AI Readiness	Limited API hooks	Native STT + LLM + TTS orchestration support
Latency Control	1.5–3 sec average	< 800 ms round trip
Programmability	Static IVR logic	Dynamic event-driven API
Integration Ease	Separate voice and data layers	Unified Realtime Voice API

Core Technical Strengths

Plug-and-Play with Any LLM: Teler works seamlessly with OpenAI, Anthropic, or custom LLMs through AgentKit.
MCP-Ready Architecture: FreJun’s event routing matches MCP standards for secure and low-latency communication.
Scalable Realtime Edge: Calls are routed globally via FreJun’s optimized edge network, ensuring reliability even during spikes.

These capabilities make Teler ideal for founders, product managers, and engineering leads aiming to deploy real-time conversational AI into real call environments.

What Does a Production-Ready Teler + AgentKit + MCP Deployment Look Like?

When deployed in production, a mature setup includes:

Containerized MCP services managed via Kubernetes.
Teler API webhooks integrated into event-driven microservices.
Automated load testing for concurrent voice streams.
Continuous monitoring dashboards for latency and call metrics.

Sample Deployment Architecture

User Call → Teler Stream → STT → AgentKit → MCP Server → TTS → Teler Playback → User

Each arrow represents a secure, real-time event stream.
Teler handles retries, playback management, and session state while AgentKit and MCP take care of logic and data orchestration.

How Can You Start Building with Teler Today?

Getting started with FreJun Teler is straightforward for both developers and product teams.

Step-by-Step Onboarding

Sign up for FreJun Teler developer access.
Generate your API keys from the developer console.
Connect AgentKit using the realtime event API.
Set up your MCP tools for core actions (CRM, scheduling, etc.).
Deploy your prototype using your preferred LLM and TTS providers.

Once integrated, you can build end-to-end AI voice agents capable of handling real customer calls – not just demos.

Why the Unified Integration Approach Matters

By combining Teler (communication layer), AgentKit (orchestration layer), and MCP (tool layer), you achieve something few platforms can deliver – a fully unified real-time AI voice system.

This architecture eliminates friction between voice infrastructure and LLM reasoning, offering:

Lower latency
Easier debugging and scaling
Seamless contextual tool calling
Consistent user experience across phone and web

Final Thoughts – Building the Future of AI-Powered Voice Communication

AI voice systems are no longer experimental; they’re becoming the operating layer for business communication.

As organizations seek to automate human-like conversations – whether for support, lead qualification, or scheduling – the stack they choose matters.

FreJun Teler provides the missing infrastructure link – a unified real-time API that connects speech, language, and tools under one protocol.

When integrated with AgentKit and an MCP server, your AI agent becomes a truly conversational entity – capable of understanding, acting, and speaking back instantly.

Ready to Build Real-Time Voice Agents with Teler?

Experience the full potential of FreJun Teler’s realtime voice API –

Schedule a demo and see how your product can deliver seamless, real-time voice communication powered by intelligent agents.

FAQs –

What is FreJun Teler used for?

Teler connects AI models to global voice networks, enabling real-time, natural conversations across telephony and VoIP.
What’s required to integrate Teler with AgentKit?

You’ll need an MCP-compatible server setup, OpenAI AgentKit credentials, and API access to FreJun Teler’s Realtime endpoint.
Does Teler support any LLM?

Yes. Teler is model-agnostic – you can connect OpenAI, Anthropic, or custom LLMs seamlessly via the MCP layer.
How does MCP enhance voice agent performance?

MCP provides low-latency bidirectional streaming, ensuring AI responses and voice playback happen instantly during live calls.
Can I connect my own TTS and STT engines?

Absolutely. Teler lets you plug in any preferred TTS/STT vendor while maintaining consistent call performance.
Is AgentKit mandatory for real-time voice integration?

No, but AgentKit simplifies session handling, event management, and real-time LLM interaction with MCP and Teler.
Does Teler handle call routing automatically?

Yes. It supports inbound/outbound call orchestration with intelligent routing APIs designed for scalable AI deployments.
How do I test my Teler integration locally?

Use Teler’s sandbox environment to simulate calls, inspect audio streams, and validate AI response times safely.
Can I deploy Teler on-premise for compliance?

Yes, enterprise clients can host Teler components privately, ensuring full data control and regulatory compliance.
What’s the typical latency in real-time interactions?

Teler maintains < 200ms average round-trip latency, enabling smooth, natural voice exchanges between users and AI agents.