Challenge
The system had to let an LLM work with a dynamic mobile interface without inventing elements, convert decisions into valid ADB commands, stop repetitive loops and request approval before critical actions.
AI AGENTS / MOBILE AUTOMATION / ANDROID
An agent that understands Android interfaces and acts inside them
AI Mobile Agent accepts a natural-language task, observes the current Android device state, selects one next action and executes it through ADB. The product supports screenshot-based visual understanding and a text-only UI-tree mode within one controlled observe, decide and act loop.
- Python 3.11+
- Android SDK / ADB
- OpenAI Vision
- OpenAI-compatible LLM
- uiautomator
- uiautomator2
- MobileUse
- pytest
Why AI Mobile Agent was created
Why AI Mobile Agent was created
Most mobile workflows cannot be automated reliably with fixed coordinates: screens change, elements move and intermediate state depends on earlier steps. The agent translates user intent into contextual actions and checks the actual interface before every move.
Who the system serves
The product serves mobile-automation teams, QA engineers and developers building operational AI agents. It works with an Android emulator or connected device where execution visibility, action boundaries and step-by-step reproducibility matter.
Solution
We implemented two observation paths: a vision agent analyzes screenshots, while a text-only agent receives a compact representation of uiautomator XML. Both operate step by step with a constrained action set, result tracking, runtime limits and a safety layer before ADB execution.
Capabilities
Product capabilities
The capabilities support one operating scenario and share common state, control and analytics.
Visual screen understanding
The vision path captures Android, sends the screenshot to a multimodal model and receives the next action with coordinates and reasoning.
Textual UI representation
The text-only path converts uiautomator XML into a compact list of elements with roles, labels, states and bounds.
Unified action set
The agent can tap, type, swipe, go back, return home, wait and terminate the task with done or fail.
Multiple LLM providers
An OpenAI-compatible client supports configurable text-model endpoints, while a separate path works with OpenAI Vision.
Controlled Telegram workflows
Dedicated commands operate only on allowed channels, verify comment context and prevent creating a channel post instead of a reply.
Complete step trace
Every step stores its screenshot, UI XML, observation, model response and executed action while removing API keys from logs.
Outcome
Outcome
AI Mobile Agent provides a unified runtime for mobile AI workflows: it observes Android through images or a structured UI tree, makes stepwise decisions, safely turns them into ADB actions and keeps a complete execution trace. Teams gain a mobile-automation foundation that can be inspected, extended and connected to different models.
Python 3.11+
Android SDK / ADB
OpenAI Vision
OpenAI-compatible LLM
uiautomator
uiautomator2
MobileUse
pytest
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