Build intelligent Android apps: Integrate into Android's intelligence system using AppFunctions Google announced that Android apps can integrate with the platform's intelligence system using AppFunctions, enabling voice and text commands to perform tasks like expense tracking, itinerary management, and note capturing faster than manual UI navigation. Ben Weiss, Senior Developer Relations Engineer at Android Developer Relations, detailed how the travel planning app JetPacker exposes features such as addExpense and getItinerary as AppFunctions, allowing system agents to execute them in the background. The Android MCP model treats apps as local MCP servers, with the platform as a central tool registry, giving developers control over which features are accessible to agents. Posted by Ben Weiss, Senior Developer Relations Engineer, Android Developer Relations Welcome back to the blog post series " Build intelligent Android apps http://android-developers.googleblog.com/2026/07/build-intelligent-android-apps-introduction-jetpack.html " where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post http://android-developers.googleblog.com/2026/07/build-intelligent-android-apps-cloud-hybrid-inference.html , we explored how to leverage Firebase AI Logic to build cloud-hosted and hybrid AI features. In this article, we'll show you how we designed and integrated these capabilities into our travel planning app, JetPacker https://github.com/android/jetpacker , using Android AppFunctions. We'll explore the rationale behind our feature choices, discuss the specialized tooling we used to accelerate development, and dive into the code that makes it all work. To select which features to provide to the intelligence system, we looked for tasks where a voice or text command is objectively faster than tapping through screens. In this side-by-side screen recording you can see this contrast perfectly: on the left, a user tapping through multiple screens to log an expense; on the right, the same task completed instantly in the background via a privileged agent. Our first choice was expense tracking. Logging a coffee expense during a trip usually takes quite a few taps—unlocking the phone, opening the app, finding the active trip, navigating to the expenses tab, tapping the add button, taking a picture of the receipt, and checking the result. By providing the addExpense and getExpenses features as AppFunctions, the system agent handles the heavy lifting. When the user says, "Add a five-dollar coffee expense to my Paris trip," the agent automatically searches for the correct trip ID in the background and inserts the expense, skipping the manual UI flow entirely. We also prioritized itinerary management. Finding what activity is next on a busy trip itinerary usually requires scrolling through a dense timeline view. By providing getItinerary and addItineraryEvent to the system, the user can simply ask, "What am I doing next in Paris?" and get an immediate answer. Finally, we focused on hands-free note capturing. Typing out reminders or notes while walking down a busy street is difficult and unsafe. Exposing a voice note capability allows the user to say, "The flight was amazing, I saw a beautiful sunset and managed to sleep well," and the privileged agent automatically transcribes and saves it directly into the travel database using the addVoiceNote AppFunction. Under the Android MCP model, your app acts as a local MCP server that exposes structured tools, while the Android platform serves as the central tool registry. On the MCP client side, agent apps are registered with the intelligence system after being granted system-privileged permissions to access the registry. When a user interacts with a registered agent, its LLM determines if the request can be handled by an AppFunction, queries the platform's metadata, and executes the appropriate registered functions in the background. This local MCP client-server design gives you full control: you choose exactly which features are accessible to the agent, keeping the rest of your app's data private. Service entry points, refining our KDoc documentation to ensure the LLM understands parameter boundaries, and setting up automated testing using ADB. Enough with the theory, let's dive into the implementation. We begin by adding the AppFunctions dependencies. One for the API and one for the Kotlin Symbol Processing compiler. implementation "androidx.appfunctions:appfunctions:1.0.0-alpha10" ksp "androidx.appfunctions:appfunctions-compiler:1.0.0-alpha10" Any custom object exchanged with the agent must be annotated with @AppFunctionSerializable . In our TripSerializable.kt https://github.com/android/ai-samples/tree/main/jetpacker/android/feature/appfunctions/src/main/java/com/example/jetpacker/feature/appfunctions/TripSerializable.kt file, we define our trip data model: @AppFunctionSerializable isDescribedByKDoc = true data class TripSerializable / The trip's unique identifier. / val id: String, / The trip's title. / val title: String, / The trip's destination location. / val location: String, / The trip's start date in milliseconds. / val startDate: Long, / The trip's end date in milliseconds. / val endDate: Long, / A list of participants. / val participants: List