Adaptive AI Agents: Handling Unexpected UI States Gracefully
Adaptive AI Agents: Handling Unexpected UI States Gracefully
NASA let AI drive the Mars rover because the alternative - waiting 20 minutes for a round-trip signal to Earth before every wheel turn - made human control impractical. The rover's AI does not have a perfect map. It encounters rocks, slopes, and terrain that nobody predicted. It adapts.
Desktop AI agents face the same problem on a smaller scale. The screen never looks exactly the same twice.
Why Scripts Break and Agents Should Not
Traditional automation records exact pixel positions or element paths. A website updates its layout, a pop-up appears, a modal shifts the button 50 pixels down - the script fails. This is why most RPA deployments have a full-time maintenance team.
An adaptive agent understands intent, not just coordinates. It knows it needs to click "Submit" - not that it needs to click at position (450, 320). When the button moves, the agent finds it. When a cookie banner covers it, the agent dismisses the banner first.
Three Patterns for Adaptive Agents
Fallback strategies. If the expected element is not visible, try scrolling. If scrolling does not reveal it, check if a modal is blocking. If a modal is blocking, close it. Each step is a reasonable recovery, not a hard failure.
State verification. After every action, check whether the expected result happened. Did clicking "Submit" actually navigate to the confirmation page? If not, investigate why before retrying blindly.
Graceful degradation. When the agent truly cannot proceed - the app crashed, the page is down, the layout is unrecognizable - it reports what happened instead of clicking randomly. A clear error message is infinitely more useful than a corrupted workflow.
The Accessibility API Advantage
Agents that use the accessibility API instead of screenshots have a structural advantage here. The accessibility tree provides semantic information - "this is a button labeled Submit" - regardless of visual layout. Themes change, fonts resize, windows move - the semantic structure stays stable.
This is why Fazm uses the macOS accessibility API as its primary input. The agent reads what apps expose through accessibility, making it resilient to visual changes that would break screenshot-based agents.
Fazm is an open source macOS AI agent. Open source on GitHub.