How Yoqa agent works

The perception–decision–action loop, available actions, speed, and limitations.

Perception → Decision → Action

On autonomous runs, the Yoqa agent operates from screenshots (not hand-written locators). On every step:

1. Perceive — capture a screenshot and analyze what is visible: buttons, text, images, current state.
2. Decide — choose the next action toward the test goal (via your configured AI provider).
3. Act — execute the gesture through Appium and observe the result.

This loop repeats until the goal is reached or the agent determines the test has failed. Per-step thoughts appear in the Runs UI so you can see why it acted.

Available actions

Action	Description
Tap	Tap any visible element; supports double tap and long press
Swipe	Swipe to navigate or dismiss
Drag	Drag from one position to another
Input text	Focus a field and type
Open link / deeplink	Navigate to a URL or in-app deeplink
Terminate / background / activate app	App lifecycle controls

Actions work across the device — including system alerts and UI outside your app — subject to local device capabilities.

Script vs AI mode

After a passing AI run, you can save a script of the executed steps. Later runs can replay the script (faster, deterministic) or continue with full AI vision. Use AI when the UI is unstable; use scripts for stable smoke paths.

Understanding instructions

Write at the goal level. Example: a long onboarding with many “Next” screens can be “Complete onboarding until the paywall” — the agent walks the screens itself.

Learn phrasing patterns in Writing good test cases.

Speed

Cold steps typically take several seconds (screenshot + model + action). Similar screens on later runs can be faster. Exact latency depends on your provider and device.

Limitations

• Timing — brief toasts or transitions may be missed between screenshots.
• Test length — keep cases under ~100 actions when possible; long flows lose context more easily.
• Local only (Phase 1) — no cloud farm yet; see Local testing.
• Provider quality — grounding and decide quality vary by model; pick vision-capable defaults.

Writing test cases

Instructions that the agent can follow

CLI for agents

When your coding agent drives each step