Real-Time Agentic AI: How It’s Driving the Next Big Shift in Mobile Apps

It’s September 11th, 2025, and Apple has just finished its yearly showcase. But let’s not talk about the new iPhone this time. Let’s lean towards something else: Live Translation for AirPods Pro, ****supported by local hardware AI capabilities. In other words, if both people wear the latest AirPods, they can speak their own language and hear the translation instantly in their earbuds. Instantly, and we mean it.

Whether you call that magic or just smart marketing**, it is a technology which uses real-time, on-device AI**, and it is just about getting more popular. Let’s take a closer look and how this solution applies for mobile apps overall.

This isn’t just a headphone story. It’s a pattern which is coming to every category of mobile app, especially increasing capabilities of frameworks like Flutter or React Native or Compose Multiplatform. It also unlocks new ways of designing experiences that feel immediate and personal.

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I am sure you have seen this meme-trend already!

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LLMs and Mobile Apps: How It Started

The first wave of AI-powered mobile applications kept the intelligence entirely in the cloud. A user's device, whether a phone or a web browser, handled the user input and some minor pre-processing. The app then used API keys for a LLM (Large Language Model) provider like OpenAI and sent requests to their servers. The real processing happened there; the app was merely a client sending data to a centralized unit with the powerful GPUs required to handle LLM requests. The result was then sent back and displayed on the device.

“A typical AI agent simply communicates with a chosen LLM API endpoint by making requests to centralized cloud infrastructure that hosts these models”  ^{Small Language Models are the Future of Agentic AI, nVidia}

This cloud-centric solution comes with challenges. Even with 5G rolling out worldwide, latency is the first major pain point. For text-based interfaces, an extra second or two might feel tolerable.

However, for digital media like real-time voice interaction, it's critical. Once you shift to streaming and instantaneous interaction, any delay feels laggy and unnatural. This is why we mentioned the new AirPods feature in the first place. It wouldn't be possible without local-first AI assistance.

You might think, "But I can already talk to ChatGPT on my phone." While that's true, it's still primarily a text-based interaction. What's really happening is that a text-to-speech service pre-processes your voice input into text before it's sent to the LLM.

While these classic AI apps with a simple cloud connection are here to stay, there is a clear need to develop new solutions for more demanding and specific tasks. To address issues of latency, cost and privacy, the industry is exploring hybrid and on-device models. A hybrid approach involves a system that dynamically selects between local and cloud models based on task complexity.

The Next Big Shift - Hybrid Approach

“While LLMs offer impressive generality and conversational fluency, the majority of agentic subtasks in deployed agentic systems are repetitive, scoped, and non-conversational, calling for models that are efficient, predictable, and inexpensive. In this context, SLMs not only suffice, but are often preferable. They offer several advantages: lower latency, reduced memory and computational requirements, and significantly lower operational costs, all while maintaining adequate task performance in constrained domains.” ^{Small Language Models are the Future of Agentic AI, nVidia}

So, here we are. This brings us to the next phase for AI-powered mobile apps: a hybrid, embedded agentic AI system. Within this approach, the mobile app utilizes a compact, on-device model and cooperates with larger, more powerful ones in the cloud only when it’s worth it.

The on-device component is a locally installed SLM (Small Language Model), a small, efficient model ideal for recognition and lightweight reasoning. It handles processes like word detection, speech-to-text, or simple planning and adjustments useful for later processing, without ever leaving the phone (or earbuds). These models are cheap to run, fast to respond, and ideal for privacy-sensitive inputs. Because they're compact, mobile app developers can fine-tune or adapt them with lightweight methods. It is practical on modern phones and wearables. It delivers efficiency and cost optimization.

On the other hand, when a task requires deeper reasoning, the local agent escalates the request to a larger cloud model. This escalation is not the default; it is a choice made in the moment based on factors like cost, latency targets, and expected value. The device is no longer just a client. It’s a planner and a router, deciding when to ask for help and when to handle things locally.

This agentic pattern also changes the application's role. Instead of hardcoding flows for every scenario, AI-powered mobile apps can expose a clean set of objectives, like “book a table,” “check stock,” or “send a payment.” The agent then learns to sequence those tools to achieve the outcome the user stated in natural language. From the user’s point of view, this means less tapping through screens and more simply saying what they want.

Synergy of technologies

Looking ahead, the best AI mobile experiences won’t be purely local or purely cloud, they’ll be co-owned. The device will handle raw input and immediate intent parsing because that’s where privacy and speed live. The cloud will enrich when there’s clear value to add: latest knowledge, heavier reasoning or more research to execute. A routing layer will evaluate the boundary in real time, balancing speed, cost, risk and expected impact on the outcome.

To build the modern AI-powered Next-Gen app you will likely want to utilize tools and architectural patterns that bring this hybrid vision to life. This isn't just about connecting to an API; it's about building an intelligent, AI-driven mobile app system. Treat on-device AI capabilities as a core feature. Turn your business logic into well-documented tools so agents can act, not just chat. Build a routing layer so you can adapt to user inputs. Make your data agent-ready, clean, balanced and discoverable.

Bottom Line

If your current architecture is “LLM in the cloud plus loading spinner” it’s time to consider extra benefits coming from the hybrid agentic AI approach.

We help teams do exactly that: define the agentic AI role in your product, build the on-device stack, wire up tools safely, and deliver a UX people actually love to use every day.

If you want your mobile app to feel alive and to keep your costs and risks grounded, let’s talk.

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