How does UiPath Agentic AI help with Ui Automation

Agentic Automation
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Hi everyone,

I’m exploring the new UiPath Agentic AI features and I’d like to understand how they specifically help with UI Automation.

Could someone please share how Agentic AI improves things like:
• Selector reliability and UI element detection
• Handling UI changes (self-healing or adaptation)
• Dynamic navigation across different screens or applications
• Reducing failures caused by layout or version changes

I’m especially interested in real examples where Agentic AI made UI automation more stable, resilient, or easier to maintain compared to traditional rule-based workflows.

Any insights, best practices, or lessons learned would be greatly appreciated.

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Hi @Dhruba_Jyoti_Kalita

Agentic AI improves UI automation by using Healing Agent to auto‑suggest new selectors, fix broken ones, add smart delays, and recover from unexpected pop‑ups, increasing selector reliability and UI detection accuracy.
It adapts to UI changes through just‑in‑time analysis and self‑healing that updates selectors and applies recovery steps, reducing failures caused by layout or version updates.
It supports dynamic navigation and stable execution by combining robust logic with AI‑based fallback strategies that keep workflows running even when the UI changes unpredictably.
For more:

If helpful, mark as solution. Happy automation with UiPath

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@Dhruba_Jyoti_Kalita

UiPath providing two features for Ui Automation reliability using AI.

For these points Healing agent is there.

For these issue UiPath ScreenPlay is there

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Here is a breakdown of how Agentic AI actually solves these common automation headaches.

  1. Reliable Element Detection (No more broken selectors)
    Traditional selectors are like GPS coordinates—if the building moves, the robot gets lost. Agentic AI (using Large Action Models) acts more like a human driver who knows what a “Bank” looks like even if the address changes.
    • Semantic Matching: Instead of hunting for a specific ID like , the AI understands the meaning of the element. If you tell it to “Click Submit,” it analyzes the screen and DOM to find the button that logically performs that action.
    • Computer Use: Using models like OpenAI’s Operator or Anthropic’s Computer Use, UiPath can now “see” the screen via screenshots. This is a lifesaver for legacy terminals or Canvas-based web apps where selectors don’t even exist.

  2. Self-Healing UI Changes
    The UiPath Healing Agent is the real heavy hitter here. It doesn’t just throw an error; it rehabilitates the workflow.
    • JIT Analysis: When a selector fails at runtime, the agent immediately compares your original intent (e.g., “I need the Email field”) with the current screen state.
    • Dual Mode Recovery:
    • Self-Healing: The bot finds the moved element, swaps the logic on the fly, and keeps the job running.
    • Recommendations: In Orchestrator, it will show you “Actionable Recommendations,” literally asking if you’d like to update the workflow with the new location it found.

  3. Dynamic Navigation with ScreenPlay
    This is where the ScreenPlay activity comes in. Traditional “Click A → Wait → Click B” flows break if a pop-up appears or a sidebar collapses.
    • Controlled Agency: You give ScreenPlay a natural language goal, like “Navigate to Billing and download the latest invoice”.
    • Resilient Navigation: If an update moves “Billing” from a top tab to a side menu, the agent “reasons” through the change, clicks the menu, and finds the text itself without you needing to re-code anything.

Real-World Example: Multi-Bank Portals
• The Problem: A bot logs into five banks to download statements. Usually, one bank changes its layout every month, killing 2-3 hours of developer time in maintenance.
• The Agentic Solution: By using ScreenPlay with a simple prompt (“Log in and download the Jan 2026 PDF”), the bot keeps working even if the bank switches to a new React-based site with dynamic IDs. Maintenance time drops to nearly zero.

Best Practices according to UiPath Documents:
Use “Micro-Tasks”: Don’t make the agent do the whole process. Use traditional RPA for fast data entry and Agentic AI for the “fragile” parts like navigation.
AI Trust Layer: Always route these activities through the UiPath AI Trust Layer to keep PII safe from the LLM.
Governance First: Start with Recommendation Mode. Let the AI suggest fixes for a week to verify accuracy before you flip the switch on “Auto-Healing” in production.
I have referred the below video:
Link: Boost automation resilience & accelerate complex workflows with UiPath Healing Agent and ScreenPlay

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