For years, automated testing teams have leaned on the Document Object Model (DOM) as their go-to framework for interacting with web applications.
But in 2025, when financial firms are deploying increasingly complex, AI-generated code across trading platforms, mobile apps and client portals, the DOM is showing its age.
Outdated testing methods not only slow down release cycles but also risk missing failures hidden beneath layers of runtime logic and modern front-end frameworks.
That’s the warning from Boris Skurikhin, co-founder at Docket, an AI-powered web testing platform.
“A common misconception in automated software testing is that the document object model (DOM) is still the best way to interact with a web application,” he said.
“But this is less helpful when most front ends are stitched together from overly complex DOMs and megabytes of runtime logic.”
AI-generated code fallout
In today’s market, software development is no longer driven purely by human engineers. Large language models (LLMs) are now responsible for generating swathes of code, a trend Skurikhin calls the “nuclear fallout of the LLM boom and the vibe-code epidemic of 2025.”
“What was once seen as arcane wizardry performed by savvy engineers is now daily output from tools like Windsurf, Claude Code and Lovable,” he wrote in a recent Forbes analysis.
But this shift does bring risks. “AI is helpful, but it doesn’t produce perfect code. In fact, LLMs make mistakes at a higher rate than thoughtful human developers. Writing code with AI today is a double-edged sword,” Skurikhin argued.
The consequences land hardest in QA. “Vibe-coded apps may appear functional in the browser, but their underlying code is often bloated, disorganized and difficult to maintain,” he said.
“I frequently see DOM trees that contain thousands of deeply nested, non-standard elements. No documentation, no spec, no clear intention, just opaque markup glued together with bulky scripts.”
For banks that have long relied on DOM-based automation frameworks, the risks are mounting.
“Many ‘AI-native’ browser automation tools still cling to the outdated idea that the DOM is a reliable source of truth,” Skurikhin explained. “That might have worked for hand-coded web apps from 2010, but today’s AI-generated UIs, minified JavaScript runtimes,
He called this the “loss chain”, the way data fidelity erodes as it flows from server to browser, through a context window and finally into an LLM.
“By the time the agent acts, it’s working from abstractions that no longer match what’s actually on the screen,” he said.
The result is unreliable automation. “Try finding the right supposed to be a login button when its click handler is buried in minified code,” he added.
“That makes it hard for automation to know what to click, and when apps rely on graphics or canvas-based interfaces, testing tools can break down into guesswork.”
Vision-based testing
Skurikhin’s answer is simple: stop looking at the DOM and start looking at the screen itself.
“Based on this, I believe the solution is computer vision: equipping automation tools with eyes and hands, screenshots and virtual peripheral devices,” he said. “This avoids the loss chain entirely by letting the AI act on what is actually rendered in the browser, not on an abstraction.”
“The AI agent is more effective when it can click the login button it sees, rather than guessing at one buried in a DOM tree that exists solely to appease the renderer,” Skurikhin continued. “For modern vibe-coded apps, the rendered screen is the source of truth, not the markup.”
Vision-based testing, however, is not a silver bullet.
“It’s true that vision-based testing is still slower, more expensive and less mature than DOM-based frameworks,” Skurikhin acknowledged.
“But that’s changing fast. Vision models are getting faster, inference costs are dropping and open-weight contenders like ByteDance’s UI-TARS are already challenging Anthropic’s CUA.”
The trade-offs depend on workload. For read-heavy processes like text ingestion, scraping or regulatory research, DOM frameworks still offer efficiency. But for highly interactive financial applications, trading dashboards, drag-and-drop tools, or graphics-intensive platforms, Skurikhin argued vision is the only reliable way forward.
“In my experience, they are great for handling things like canvases, iframes, scrollable containers, drag-and-drop actions and nonstandard input components,” he said.
For Skurikhin, the lesson is clear for banks navigating the AI era of software development and QA. “Computer use is inherently visual. We design interfaces for humans to look at and interact with using a mouse and keyboard. If AI aims to be the brain, it needs eyes and hands,” he said.
“In the age of vibe-code, don’t lock yourself into a DOM-shaped view of the world,” Skurikhin concluded. “Build tools that see.”
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