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To build an AI agent that completes tasks across mobile apps, combine an orchestration loop with the right execution route for each task: call structured app or backend functions when they exist, and use screenshot-driven UI automation for workflows that lack an integration. The model should propose bounded actions; your client must execute them, check what actually happened, and pause for user control when needed. Android documents several distinct approaches, but the available evidence does not establish a general-purpose third-party API for arbitrary cross-app UI control on iOS.
Start with the execution route, not the model
A language model alone cannot complete a mobile task. The agent needs access to an execution environment and a loop that observes state, selects a next step, carries it out, and checks the result. For a request such as “Add milk and eggs to my grocery cart,” that may mean calling a grocery app’s supported function, or interacting with its interface if no suitable function exists. “Book a ride to the airport” and “Find me a flight from SF to Hawaii” are other illustrative prompts; each needs the user’s missing details clarified before consequential steps.
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Choose integrations app by app and workflow by workflow. Prefer a narrow, explicitly exposed function for a stable business operation; use UI automation to cover gaps, understanding that it must interpret screens and can act on stale or ambiguous state.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Route | What it does | Best fit | Main limitation |
|---|---|---|---|
| App-provided function | Invokes a discrete capability deliberately exposed by an app. | Well-defined operations whose inputs and outcomes have stable meaning. | The app owner must expose the capability; it does not cover arbitrary interface workflows. |
| Remote MCP server | Makes backend functions available to an agent. | Service operations that can be provided by a backend, including across platforms. | The service owner must provide and authorize the integration; it is not UI control. |
| Screenshot-based UI automation | Uses screen images to propose interface actions such as clicking, scrolling, or typing; a client executes them. | Workflows for which no suitable structured integration exists. | Screen interpretation, coordinates, layout changes, and UI state can cause errors. |
| Agent-to-UI | Lets an agent generate structured interfaces for an app to render. | Cases where the app should present an agent-generated interface. | It is a distinct Android approach, not a substitute for app functions or general UI automation. |
| Android Computer Control | An upcoming route for OEM-preloaded assistants to interact with app interfaces without explicit changes to the target apps. | Potential platform-level UI interaction by supported preloaded assistants. | It is described as upcoming, not a universally available developer API. |
Android’s overview lists AppFunctions, remote MCP servers, upcoming Computer Control, and Agent-to-UI as separate routes, and describes the wider effort as early-stage and Preview. Its stated aim is to explore the paradigm with privacy and security in mind, not to promise that every route is generally available. See Android’s app-to-intelligence overview.
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Build a bounded observe–act–verify loop
Keep planning separate from execution. A useful loop treats each action as a proposal until the client has performed it and checked a fresh result.
- Clarify the goal. Turn the request into a task with a defined end state. Ask for missing information such as a destination, date, item size, quantity, or preferred option rather than guessing.
- Choose the narrowest permitted operation. Use an authorized app function or backend tool if it covers the next step. Otherwise, select a UI action that advances the task without exceeding the user’s request.
- Observe current state. Gather the function’s available state or a fresh screenshot. Do not treat an old screen or the model’s memory of an earlier step as proof of the current state.
- Propose and validate one bounded action. Have the model return a specific function call or UI action. Check that it is within the task’s scope and that any required safety decision permits it. For a consequential action, obtain user confirmation before execution.
- Execute in the client environment. The model’s response is not itself a completed action. Your client adapter must carry out the approved call or UI operation in the target environment.
- Refresh state and check the postcondition. Confirm that the intended change occurred—for example, that the correct item and quantity are in the cart—before proceeding or reporting success.
- Recover conservatively or stop. Retry only if fresh state shows a safe, recoverable failure. If the screen is ambiguous, permission is denied, or the task has drifted out of scope, pause, explain the issue, or hand control back.
- Report the outcome. Tell the user what was verified and what remains incomplete; do not claim success based only on a proposed action.
For screenshot-driven actions
Google’s Gemini API documentation describes screenshot input, suggested UI calls, client-side execution, and a repeated screenshot/result cycle. The model proposes actions; the developer’s client executes them in the target environment. Its browser and Playwright sample shows normalized coordinates scaled to a viewport, but that implementation example does not establish that every mobile operating system exposes the same control mechanism. Account for viewport dimensions, scaling, scroll position, and fresh screen state rather than assuming a coordinate has a fixed meaning. The documentation labels Computer Use Preview and warns of errors and security vulnerabilities; see Gemini API Computer Use.
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Design permissions and human control into the task
Permission should be specific to the operation and the app, not an open-ended license inferred from the user’s overall goal. Separate the model’s job of interpreting the request from the system’s decision about whether an action is authorized. Screen contents are untrusted input: text in a webpage, email, document, app, or multimedia can contain malicious instructions that attempt to redirect the agent toward purchases, messages, or account changes.
- Limit available app functions and UI actions to what the task requires.
- Require review or explicit confirmation for sensitive data entry, sign-in, account creation, messaging, address changes, purchases, and final submission when appropriate.
- Provide a persistent way to stop the agent or take over the device.
- Make the authorization and confirmation outcome binding: do not execute an action the safety layer blocks or marks as requiring confirmation until that confirmation is received.
- Verify the resulting app state, especially after an action with financial, personal, or difficult-to-reverse consequences.
Google’s Gemini Apps Help describes a consumer screen-automation beta for select Android apps, with per-app permission to read and interact with screen content. It warns about mistakes such as selecting the wrong item, entering an incorrect quantity, claiming completion prematurely, or causing an unintended purchase; it also describes monitoring and user takeover. That consumer feature is not a general developer API. At the time described on the help page, eligibility was limited to users aged 18 or older in the US or Korea, required a personal Google Account, and listed English and Korean. The listed devices were Pixel 10, Pixel 10 Pro, Pixel 10 Pro XL, Samsung Galaxy S26 series, Z Flip 8, and Z Fold 8; Pixel 10 was unsupported in Korea, and rollout was gradual. These rollout details can change; consult the current Gemini Apps Help page for consumer availability.
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For the developer Computer Use API, Google recommends close supervision for important tasks and advises against relying on it for critical decisions, sensitive data, or actions where serious errors cannot be corrected. A safety result can allow an action, require confirmation, or block it; the client needs to honor that result. Google DeepMind announced on June 24, 2026 that computer use was built into Gemini 3.5 Flash, with optional enterprise safeguards. Model and API availability can change, so check the announcement and current API documentation before choosing a production dependency.
Treat Android and iOS as different platform questions
Android
Android’s current overview presents multiple routes with different roles: AppFunctions for discrete on-device capabilities, remote MCP servers for backend integration, upcoming Computer Control for OEM-preloaded assistants, and Agent-to-UI for agent-generated structured interfaces. Do not treat those as interchangeable, or assume the preview-stage overview means every developer can use every route today. Select according to the capability you need and confirm current platform availability in the official Android documentation.
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iOS
The available first-party documentation here does not establish a general-purpose API that lets third-party agents arbitrarily control other iOS apps. Do not promise Android-style UI automation on iOS or give a production integration plan based on that assumption. Treat iOS app functions and any supported integrations as app- and service-specific, and verify any proposed cross-app control mechanism against current Apple documentation before committing to it.
Test task completion and recovery, not just successful demos
Build a repeatable suite around actual user workflows. Include one-app and multi-app sequences, app switching, ambiguous search results, permission denial, interruption, stale state, interface changes, and user takeover. Measure independently:
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- Whether the intended task completed.
- Whether the final postcondition was correct.
- Whether any unintended action occurred.
- How many steps were needed, and how often the agent recovered safely.
- How often a person had to intervene.
Exercise supported simulators and, where real-device behavior matters to the product, physical devices representative of its intended deployment. A successful single run does not establish reliability across apps, devices, or changed interfaces.
Use benchmark results as context, not a product promise
The 2026 iOSWorld paper reports a benchmark of 133 tasks across 26 native iOS simulator apps, including 60 multi-app tasks spanning two to eight apps and 46 memory or personalization tasks. Its best reported configuration scored 52% overall and 37% on multi-app tasks; privileged vision plus XML input improved frontier-model performance by up to 26 percentage points in that evaluated setup. These are results from the paper’s benchmark and systems, not forecasts or guarantees for a deployed agent. The benchmark measures task performance; it does not establish permission or API availability for arbitrary third-party iOS UI control. Read iOSWorld: A Benchmark for Personally Intelligent Phone Agents.
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