Mobile applications are evolving beyond traditional screen-based interactions. Instead of opening an app, navigating multiple menus, and completing every step manually, users increasingly expect to describe what they want and let AI handle parts of the process.

For example, someone might ask an AI assistant to find a suitable restaurant, check available options, and prepare a booking. Completing this workflow may require the assistant to interact with multiple applications and services.

This shift is creating interest in agentic applications, where AI systems can understand goals, select appropriate tools, and perform multi-step tasks.

Google introduced several important developments for this direction at the Android Developer Summit on November 5, 2026. Among them, Android MCP, ML Kit's Agentic API, and the A2UI Compose renderer provide new ways to connect AI agents with Android applications.

What Are Agentic Apps?

Traditional applications generally follow predefined interaction flows. Users tap buttons, enter information, navigate screens, and trigger individual actions.

AI-powered applications introduce another interaction model. Instead of requiring users to follow every step, an AI agent can interpret a request, identify the necessary actions, and coordinate the workflow using available tools.

Consider a fitness application. A user might ask:

"Create a weekly workout schedule based on my available time."

An agentic experience could:

  • Understand the user's request.
  • Retrieve relevant fitness preferences.
  • Identify suitable workout options.
  • Generate a weekly schedule.
  • Present the result through the application's interface.

The agent does not simply generate a text response. It coordinates multiple actions using functions exposed by the application.

However, the application still needs to define which actions are available and enforce appropriate permissions. AI should not receive unrestricted access to sensitive user information or critical operations.

What Is Android MCP and Why Does It Matter?

Model Context Protocol (MCP) provides a standardized approach for connecting AI models with external tools and data sources.

Google's Android MCP builds on this concept to help AI agents interact with application capabilities on Android devices.

Through the AppFunctions Jetpack library, developers can expose specific application functions that qualified AI agents can discover and invoke.

For example, an e-commerce application could expose functions for:

  • Searching available products.
  • Checking product information.
  • Retrieving order status.
  • Adding items to a shopping cart.

When a user requests something, an AI agent can identify the relevant function and use it to complete part of the workflow.

Android MCP is particularly useful for workflows involving local device capabilities, sensitive device data, hardware access, or operations that need to execute locally. Remote MCP servers can also support cross-platform integrations.

Google describes Android MCP as part of its broader Android intelligence system, designed to connect AI agents with supported applications and services.

AppFunctions: Connecting Android Apps with AI Agents

AppFunctions is an important component of Android's agentic application architecture.

It allows developers to expose selected application capabilities through structured functions that AI agents can use.

For example, a travel application might provide functions for searching destinations, retrieving saved trips, or checking booking information.

Instead of navigating every screen manually, an AI agent can select the appropriate function based on the user's request.

Developers need to define clear function descriptions, input parameters, and expected outputs. This helps agents understand which operations are available and when they should be used.

Google announced AppFunctions in beta as part of its October 2026 Android development updates. Developers can begin preparing integrations, although availability across system agents and production surfaces remains subject to Google's preview and rollout requirements.

Building On-Device Agents with ML Kit's Agentic API

Another significant development is the Agentic API in Google's ML Kit GenAI offering.

It enables developers to create multi-step AI workflows directly within Android applications using Gemini Nano and the Android AICore system service.

The API provides an orchestration layer that can manage several parts of an agentic workflow:

  • Understand user instructions.
  • Select and execute registered functions.
  • Maintain conversation context across interactions.
  • Process function results.
  • Continue the workflow until a defined stopping condition is reached.

For example, a productivity application could use an on-device agent to organize a user's tasks based on their instructions and available application data.

An important advantage is that the reasoning and planning process can run locally on supported devices. This can reduce network dependency and help keep prompt information on the device.

However, local execution does not automatically mean every operation is private. Functions implemented by the application may still communicate with external servers or cloud services.

The Agentic API is currently in alpha. Google's documentation also warns that model-generated tool selections and arguments can be incorrect, making developer validation and safety controls essential.

Source: Google ML Kit: Build Agentic Workflows with Gemini Nano.

A2UI: Turning AI Responses into Interactive App Interfaces

Most conversational AI experiences return information as text. But many application workflows require more than a written response.

Google's Agent-to-UI (A2UI) approach addresses this limitation by allowing AI agents to generate structured interface descriptions that can be rendered using native application components.

The Jetpack Compose A2UI renderer allows agents to stream interactive UI experiences using native Compose and Material 3 components.

For example, imagine a user asking a shopping application to find a laptop within a specific budget.

Instead of returning a long text response, an agent could present an interactive interface containing product options, comparison details, and selection controls.

The application can use its own approved components to display the generated experience.

This approach can make AI interactions more practical because users can review information and interact with the interface rather than relying entirely on conversational responses.

The A2UI Compose renderer is currently in alpha, so developers should consider its experimental status before adopting it in production applications.

Important Challenges Developers Need to Consider

Although agentic applications introduce new possibilities, they also create technical and security challenges.

1. Function permissions and validation

Developers should expose only the functions required for a specific user experience. Every function call should validate its parameters and confirm that the user has permission to perform the requested action.

2. Unintended AI actions

AI models can misunderstand requests or select incorrect tools. Sensitive operations, such as payments, account changes, or bookings, should require appropriate confirmation before execution.

3. Performance and resource management

On-device AI requires processing power, memory, and battery resources. Developers need to test performance across supported devices and establish limits for multi-step workflows.

4. Data privacy

Applications should avoid exposing unnecessary personal information to AI agents. Sensitive operations need clear permission boundaries and appropriate data-handling practices.

5. Testing and reliability

Agentic workflows can follow different paths depending on user instructions and available information. Testing should cover incorrect inputs, failed function calls, interrupted sessions, and unexpected model responses.

Conclusion

Android MCP and agentic application development represent an important change in how people may interact with mobile software. With AppFunctions, ML Kit's Agentic API, and the A2UI Compose renderer, developers have new ways to connect AI reasoning with application functionality and native interfaces.

However, building useful agentic applications requires more than integrating an AI model. Developers must also consider architecture, permissions, performance, privacy, and reliable execution.

Businesses planning to build intelligent Android applications need a development approach that combines AI integration with strong engineering practices. Working with an experienced mobile app development company can help businesses plan, develop, and maintain applications that are prepared for emerging technologies and evolving user expectations.