Test Execution

Command-first automation logic with layered fallback: resolves UI elements by ID or text, then uses AI for retries, scrolling, and popups. Built-in fail-safes and visual model support included.

🔁 Command Handling

When a test step includes a supported command, the system always processes it first — before using any AI-based behavior.

• Commands must follow the specific syntax defined in the Commands Reference.

• During this phase, the system tries to resolve the target element by:

  • Searching for an element ID

  • Or matching visible text from the UI hierarchy

• This search lasts up to 5 seconds.

• During this phase, no AI models are used.

If the element cannot be found within this initial phase, the system then proceeds to AI-based handling (described in AI Decision-Making).

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🧩 Element Detection Logic

Before executing a command, the system first attempts to locate the relevant UI element using element ID or visible text — without invoking AI. The detection process works in stages:

1. Primary Lookup – The system first searches for elements using:

   1. Internal element IDs

   2. Visible text on the screen

   This stage lasts for up to 5 seconds.

2. Fallback to AI – If no matching element is found within that time:

   1. The system triggers AI-based assistance (see next section).

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🤖 AI Decision-Making

If traditional lookup fails, AI steps in to try to accomplish the intended action:

• Wait & Retry: AI will wait up to 3 seconds, up to 2 times, to give the UI a chance to settle.

• Scroll Behavior: If it suspects the needed element is off-screen, it will automatically scroll.

• Popup Handling: AI can dismiss overlays, modals, or popups if they appear to block interaction.

• Goal-Oriented: The AI's decisions are guided by an understanding of what the test step is trying to achieve — not just what’s on screen.

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🧷 Screen Stability Handling

Before taking any further action after a command or AI intervention, the system checks for UI stability:

• It waits up to 3 seconds for the screen to stabilize.

• A screen is considered unstable if there are ongoing animations, loading spinners, or blinking cursors.

• If the screen hasn’t stabilized within the 3-second window, the system proceeds anyway, assuming forward progress is better than stalling indefinitely.

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🧱 Fail-Safe Conditions

To ensure reliable, predictable behavior, the system enforces several hard limits:

• Cloud Run Time Limit: Automation runs will fail if they exceed 60 minutes.

• Identical Screenshots: If the same screenshot appears 10 times in a row, the test is marked as failed (assumed to be stuck).

• Repeated Step Count: If the same test step number executes 10 times, the run is failed to avoid infinite loops.

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🧠 AI Model Stack

AI plays a key role in automation resilience. The system uses a combination of specialized models:

• OCR (Optical Character Recognition) – For reading on-screen text

• UI Element Detection – For identifying components like buttons, inputs, toggles

• Icon Recognition – For detecting familiar UI icons

• LLMs (Large Language Models) – To understand test prompts and determine next actions

➕ withVision: Mode

When the withVision: feature is used:

• A single, powerful model is used that can natively understand visuals.

• Ideal for visual assertions, such as:

  • Checking color schemes

  • Verifying layout alignment

  • Detecting images or custom graphics

• Reduces the need to stitch together results from multiple models.