Posted by Jingyu Shi, Staff Developer Relations Engineer
At Google I/O 2026, we introduced Android’s shift from an operating system to an intelligence system. We also demonstrated how you can build intelligent experiences natively with the system and bring the power of Google’s AI into your apps. If you missed these updates, check out our quick recap video here:
1. Putting your apps at the center of the intelligence system
The Android OS already enables agents like Gemini to complete task automation, where it can navigate an app on the users behalf.
AppFunctions (Android MCP) provides you with more control over how your app integrates with the intelligence system. This new platform API and Jetpack library are currently available in experimental preview.
- Android MCP: AppFunctions allows your application to act as an on-device Model Context Protocol (MCP) server. It means you seamlessly share your app’s tools, services and data to the system and agents.
- Streamlined Development: You can leverage the new skill to easily generate AppFunctions within your codebase.
- Exploration and Testing: We’ve released a new test agent that allows you to experiment and debug your AppFunctions in a simulated agent environment.
To see it in action, check out the live demo showcased during the What’s New in Android presentation.
2. On-Device Power with Gemini Nano 4 Preview
Last month, we launched Gemma 4, our state-of-the-art open models. You can already preview and prototype with the next generation of Gemini Nano (Nano 4) models with the AIcore developer preview. To make productionizing with Gemini Nano more reliable and performant, we are adding a few new features in ML Kit GenAI APIs:
- Prototype to Production: Transition from prototyping in the AICore Developer Preview to building production-ready apps using the ML Kit GenAI Prompt API to leverage Gemini Nano 4 that’s launching in flagship devices later this year.
- Structured Output: The upcoming Structured Output API will allow you to define object classes to be returned as outputs from Prompt API, ensuring reliable outputs in productionizing your intelligent features.
- Prefix Caching: It optimizes your on-device inference performance with the prompt API. The new Prefix caching reduces inference time by storing and reusing the intermediate LLM state of processing a shared and recurring part of the prompt.
For highly customized or niche use cases, you can also use LiteRT-LM to bring your own fine-tuned small language model to Android.
3. Hybrid Inference & Agents
To help you build more advanced AI features like hybrid inference and explore building in-app agents, we’ve released new APIs, framework and guidances:
- Firebase AI Logic Hybrid Inference: This new API provides the simple routing capability between on-device models and powerful cloud infrastructure. You can set explicit orchestration modes, such as
PREFER_ON_DEVICE,PREFER_CLOUD,ONLY_ON_DEVICE, orONLY_CLOUD, based on your need.
- A2UI Jetpack Compose Renderer: The new A2UI library allows your agents to « speak UI ». With the upcoming Jetpack Compose Renderer, you can automatically render these A2UI messages as native UI components.
- ADK for Android: The first version of ADK for Android is available for experimentation. It allows you to build multi-agent workflows across both on-device and Cloud models while managing orchestration, context handling and sessions between agents.
From building with on-device models, exploring hybrid inference to building agents, you can see them in action in this talk:
Start Building Today
Whether you are experimenting with AppFunctions to prepare for the intelligence system, or looking to bring the power of Google’s AI within your own app, we’ve got you covered. Dive deeper into the code snippets, samples and comprehensive developer guides on the Android AI hub. For the full breakdown of what’s new, check out the official AI on Android at Google I/O 2026 playlist.
We are excited to see what you build!