The rise of AI-driven customer interactions has made voice agents an essential part of modern business. But building them for one region is very different from running them across the world. When conversations travel over a mix of PSTN lines, VoIP networks, and cloud telephony systems, the real challenge is not only what the agent says but how reliably and quickly the voice reaches the user. Latency, codec mismatches, and compliance add complexity at every step.
This blog explores exactly how to run voice agents across global networks – breaking down the technical pipeline, highlighting key challenges, and sharing best practices.
For teams looking to simplify the voice layer, global voice infrastructure for AI agents provides the foundation.
Why Voice Agents Need a Global Network
Running a voice agent in one country is fairly straightforward. You connect a local phone number, route calls through a single carrier, and ensure the agent responds in real time. But the moment you expand beyond borders, the complexity grows.
Now you are dealing with differences between local PSTN lines, multiple VoIP networks, and distributed cloud telephony systems. Each network behaves differently. Latency that feels invisible in a local call suddenly becomes disruptive when the caller is in London and the server is in California. Regulations also vary from one country to another, which affects how you capture, store, and process audio.
The global scale problem is not the AI model itself. Models can be trained or hosted anywhere. The real challenge lies in transporting the voice reliably, across thousands of network conditions, while still keeping conversations natural.
This is why designing for a global network is the foundation of running effective voice agents.
What Exactly Is a Voice Agent
A voice agent is more than just a chatbot with speech. It is a system that takes spoken words from a human, interprets them, decides how to respond, and sends back speech that feels conversational.
The typical pipeline looks like this:
| Component | Function |
| Speech-to-Text (STT) | Transcribes spoken words into text |
| Reasoning engine | Processes the text, applies logic or queries knowledge sources |
| Text-to-Speech (TTS) | Converts text responses into audio |
| Action layer | Executes tasks like updating a record or booking a slot |
| Transport layer | Handles call setup, audio streaming, and network interoperability |
Each layer must work with the others in real time. If the speech-to-text step lags or if the transport layer introduces jitter, the whole experience suffers.
This is why teams often underestimate the engineering effort. Building the reasoning logic is only one part. Running it smoothly on top of diverse telephony systems and VoIP networks is the harder problem.
How Voice Agents Work Over VoIP and Cloud Telephony
Let’s follow the path of a single call to see what happens technically.
When a person speaks into their phone, the audio is first encoded into a codec supported by their local network. A landline may use G.711, while a VoIP app might use Opus. The audio then travels across the carrier infrastructure until it reaches a cloud telephony system.
That cloud telephony system is responsible for:
- Setting up the call session
- Handling signaling (who is calling, where to connect)
- Streaming the audio packets to your application
- Receiving audio back and sending it to the other end
Your application then takes this audio stream and pushes it through the STT engine, followed by the reasoning engine, then the TTS engine. Finally, the converted speech is streamed back over the telephony network to the caller.
In one recent telecom-voice-agent pipeline, the full round-trip from speech to response averaged 0.94 seconds, with ASR at 50 ms, TTS 280 ms, and LLM inference 670 ms. This sets a benchmark for global systems aiming for human-like voice latency.
Every hop matters. Codec conversions can introduce artifacts. Buffering can delay responses. Even a 200 millisecond network delay can double once the return path is added. This is why engineers aim for an end-to-end response time of under 500 milliseconds. Anything longer makes the conversation feel artificial and interrupts natural flow.
The Challenges of Running Voice Agents Across Global Networks
Latency and flow of conversation
In a face-to-face conversation, humans take roughly 300 to 500 milliseconds to respond. This is the natural rhythm of dialogue. If a voice agent takes longer, the caller perceives it as slow or unresponsive.
Typical industry standard for VoIP one-way latency is less than or equal to 150 ms; exceeding that in one hop (or round-trip) begins to noticeably degrade voice quality.
When calls cross continents, network latency alone can consume half of that budget. Add processing time for transcription, reasoning, and synthesis, and the limit is quickly reached. Designing for low-latency pipelines is therefore critical.
Codec and media handling
Different networks rely on different codecs:
- PSTN typically uses G.711
- Mobile networks may rely on AMR or EVS
- VoIP networks often use Opus or G.729
Each codec has its own compression rules. If audio is transcoded multiple times between codecs, the result is both degraded quality and additional delay. Avoiding unnecessary transcoding steps is a major technical requirement when building reliable voice agents.
Regulations and compliance
Every region enforces its own voice communication rules. In Europe, GDPR restricts how audio recordings and transcripts are stored. In the US, TCPA rules apply to outbound calls. Some countries demand that call data never leaves local servers.
This means your architecture must not only be technically efficient, it must also align with legal requirements in every geography you operate. Failing to design for compliance can result in fines and blocked services.
Reliability at scale
A proof of concept may handle a few hundred calls per day without issue. Scaling to millions of minutes across different countries is another challenge altogether. Call drops, packet loss, and routing errors increase as you add volume. Monitoring systems, fallback strategies, and failover routing are mandatory to keep global deployments stable.
Maintaining conversational context
Long calls are not just about low latency – they require context continuity. When a caller explains their issue in three steps, the voice agent has to keep track of each turn, even if the call is routed through different nodes. The more hops and systems in the chain, the harder it becomes to maintain that continuity without introducing errors.
Step-by-Step: Running a Voice Agent Across Global Networks
- Call initiation: The process starts when a caller dials a phone number or connects through a VoIP client. At this stage, the telephony system is responsible for setting up the session, establishing the pathway that will carry audio between the caller and your application.
- Audio capture and transport: Once the call is live, the caller’s speech is encoded into the appropriate codec and streamed across the network. Cloud telephony systems manage this process, ensuring that the audio reaches your backend with minimal packet loss or jitter.
- Speech recognition: The audio packets are fed into a speech-to-text (STT) engine, which converts spoken words into text in real time. Modern STT solutions provide incremental transcripts, so words appear as the caller speaks, rather than waiting until the sentence ends. This reduces lag and makes the interaction feel natural.
- Reasoning and response generation: The transcribed text is passed to the reasoning engine – an LLM or other AI logic – that interprets the caller’s intent. Based on this input, the engine decides what response to give or what action to execute, such as fetching account details or booking a slot.
- Text-to-speech synthesis: Once the response is ready, it is converted into spoken audio through a text-to-speech (TTS) engine. The quality of the TTS voice is critical, as natural intonation and pacing are what make the agent sound human-like and engaging.
- Return streaming: The generated audio is streamed back through the telephony path to the caller. This must happen in real time, so that the response arrives quickly and without noticeable delay, preserving the conversational flow.
- Context management: Throughout the call, the system maintains conversational state. This means remembering what the caller said earlier, tracking open questions, and handling any external data queries in the background. Context management is what makes multi-turn conversations possible.
This single loop – caller speech, AI reasoning, and voice response – repeats continuously until the call ends. For the user, it feels like speaking with a responsive agent. For engineers, it is the precise orchestration of telephony, audio processing, and AI components across networks and regions.
Explore how Deepgram AI powers real-time speech recognition, automates calls, and delivers accuracy that transforms global business communications.
How FreJun Teler Solves the Global Voice Layer
Everything discussed so far points to one conclusion: building and maintaining a global voice transport system is a massive undertaking. Even large enterprises struggle with it because it requires carrier integrations, distributed infrastructure, codec management, and compliance frameworks.
This is the gap that FreJun Teler fills. Teler provides the global telephony and voice transport infrastructure while leaving full control of the conversational logic to you. Whether you choose OpenAI, Anthropic, LLaMA, or a domain-specific model for reasoning, and whichever STT/TTS stack you prefer, Teler ensures the voice loop is reliable.
Key advantages:
- Model-agnostic integration: Bring your own AI stack, Teler handles the connectivity.
- Low-latency media streaming: Designed to keep end-to-end response times within conversational limits.
- Global reach: Works across PSTN, VoIP networks, and cloud telephony systems, so the same agent can take calls from anywhere.
- Developer-friendly SDKs: Ready-made libraries to embed voice capabilities in mobile, web, or backend apps.
- Enterprise-grade compliance and reliability: Built-in support for data integrity, uptime, and routing policies across jurisdictions.
In short, Teler abstracts away the hardest part of running voice agents globally: the voice transport layer.
Use Cases That Benefit From Global Voice Agents
Running voice agents across borders is not just about scale – it is about unlocking entirely new applications. Some of the most impactful use cases include:
AI-powered reception and support
Companies can replace or augment front-line support with voice agents that answer calls 24/7, in multiple languages, and escalate only when human intervention is needed.
Outbound campaigns at scale
Appointment reminders, lead qualification, or customer surveys can all be handled by voice agents that feel personal, yet operate across continents.
Industry-specific operations
- Healthcare: patient triage, appointment scheduling.
- Finance: KYC verification, account status updates.
- Travel and logistics: booking confirmations, dispatch coordination.
Each of these requires reliable voice connectivity across diverse telephony systems – exactly where Teler adds value.
Forecasts suggest that by 2034, the cloud telephony market will reach nearly USD 59.5B, growing at over 9 % annually – pointing to rapid modernization of voice networks worldwide.
Technical Best Practices for Running Global Voice Agents
Even with a strong infrastructure layer, success depends on how you design your deployment. The following best practices help maintain quality at scale:
Keep media streams regionally close
Where possible, host STT, TTS, and reasoning engines in regions close to your users. This minimizes latency.
Avoid unnecessary transcoding
Choose codecs that align with your main carrier networks. Each conversion step adds delay and reduces clarity.
Implement monitoring and observability
Track call quality metrics such as packet loss, jitter, and round-trip latency. This allows proactive issue resolution.
Plan for compliance and data localization
Store audio and transcripts in regions that satisfy local regulations. Design routing policies accordingly.
Build graceful fallback flows
When an STT or LLM provider has degraded performance, the agent should still function in a reduced capacity rather than fail completely.
By following these principles, teams ensure that the voice agent experience remains natural and reliable regardless of geography.
Learn the essential factors to consider when choosing the right AI tool for building powerful, reliable, and scalable voice bots.
Conclusion
Running voice agents across global networks is not just about building accurate speech recognition or advanced reasoning models. The harder challenge is transporting live voice reliably across PSTN, VoIP, and cloud telephony systems while keeping latency low and dialogue natural.
FreJun Teler takes this complexity away. With its model-agnostic, low-latency infrastructure, you can connect any STT, TTS, or LLM while Teler ensures global voice transport is seamless and compliant. Your team stays focused on building intelligent, context-aware logic – while Teler handles the voice backbone.
Want to see it in action?
Schedule a demo now to experience a live call setup, API walkthrough, and real-time integration of your AI pipeline with global voice networks.
FAQs
What latency is acceptable for a voice agent?
For natural dialogue, the loop from caller speech to agent response should remain under 500 milliseconds. Beyond that, pauses feel unnatural.
Can I use any LLM with a voice agent?
Yes. The voice transport layer is separate from the reasoning engine. You can choose GPT, Claude, LLaMA, or domain-specific models.
How do cloud telephony systems fit in?
They act as the bridge between the traditional PSTN world, VoIP networks, and your AI backend. They handle call setup, media streaming, and routing.
Why not just use an existing contact center platform?
Contact center platforms provide workflows and dashboards but are not designed for flexible AI pipelines. If you want to plug in your own models, you need a lower-level voice transport API.