How Websites Actually Behave

Good scraping tools don't just extract data; they ensure the complete and precise discovery of all available data. To achieve this, they must remain reliable, easy to operate, and flexible enough to handle diverse website structures, from simple static pages to complex, dynamic, or irregularly paginated layouts. Whether for price intelligence, competitive analysis, or large-scale research, the value of a dataset is determined by its completeness. However, achieving 100% coverage is an architectural challenge because websites use widely different pagination patterns and page-loading behaviors.

Standardizing the web is deceptively difficult. When developing the Listing Agent, we encountered massive variance in how modern websites structure their data fetching and navigation. A naive scraper expects a static page, but the modern web has dynamic environment of Single Page Applications (SPAs), content rendered progressively, and obfuscated navigation. Many sites use lazy rendering, where only an initial skeleton of content loads and the rest appears gradually, triggered by scrolling, clicking, or other interactions. To handle this, the Listing Agent mimics human behavior with gradual, iterative scrolling, allowing the site’s JavaScript to fetch additional items in real time. This ensures every listing is revealed, improves reliability of content capture, and reduces anti-bot detection by simulating natural scroll patterns with variable speed and pauses. Some pages combine both approaches, serving a few items server-side while hiding dozens more behind lazy loading, making behavior detection crucial for complete data extraction. Our research found that listing navigation generally falls into three distinct archetypes:

1. Traditional Pagination : The classic "Next" button or page numbers (1, 2, 3...).

2. Infinite Scroll : Content appends automatically as the viewport descends.

3. Load More : A button explicitly requests the next batch of data to append to the current DOM.

Our internal dataset of over 1,356 analyzed domains shows that the web splits into 3 dominant pagination patterns, Next-Button Pagination, Infinite Scroll, and Load-More Button. This dataset reveals a fractured landscape roughly 71% of sites still rely on Traditional Pagination, a pattern predominantly found in older architectures or high-SEO sites where distinct page URLs are preferred. Meanwhile, 17% have shifted to Infinite Scroll, now dominant in social platforms and modern feed-style commerce to maximize engagement. The remaining 12% utilize Load More buttons, where user intent is required to append data to the current view. This diversity implies that a rigid, rule-based scraper is destined to fail; an effective agent must be fluid and adaptive.

How We Build Listing Agent That Discover Variety of Website Structure

To solve the discovery problem while keeping the tool accessible to beginners, we needed a way to automatically detect pagination patterns without requiring users to write complex rules. This led us to develop a system centered on Pagination Type Detection and Lazy Rendering Handling, enabling the crawler to infer navigation behavior directly from a page’s structure.

One of our core engineering insights was that scanning the entire DOM for navigation cues is computationally heavy and highly prone to noise. Raw HTML contains thousands of irrelevant tokens such as analytics scripts, heavy SVG paths, and base64 images. Sending this unfiltered content to a classification model increases latency and decreases accuracy.

To address this, we engineered an in-house HTML Cleaner, specifically designed through our research to truncate and preserve only the structural content that matters. The cleaner not only removes unnecessary elements but also applies a truncation threshold, so if the cleaned HTML exceeds a pre-defined character limit, the remaining content is truncated. This approach preserves performance and keeps classification cost predictable. Our cleaner operates through two distinct phases before any classification occurs:

1. Noise Elimination, The system strips non-structural elements. Tags like script, style, svg, and iframe are removed, along with semantic regions like header or aside that rarely contain pagination. We also trim attributes, removing tracking metadata and shortening long URIs.

2. Structural Collapse, Listing pages are defined by repetition—hundreds of product cards or table rows. We don't need to read every item to know a list exists. Our cleaner generates an "Element Signature" for each node (tag + class + attributes). When it detects long runs of identical signatures (e.g., 50 repeated div.product-card), it collapses them into a single placeholder. This preserves the structure of the list while reducing HTML size by up to 90%.

Once we obtained a clean HTML structure, one challenge remained: truncation could unintentionally remove critical pagination elements. Based on our research across 1,356 websites, we found that 98% of “Next” buttons and other pagination controls are located at the bottom of the HTML document. To maintain optimal speed, accuracy, and completeness, we therefore apply a length threshold that preserves the bottom portion of the HTML, ensuring these navigation elements are retained.

This large-scale analysis revealed a consistent pattern: pagination and navigation logic almost always reside in the lower segments of the document. Building on this insight, we adopt a bottom-focused extraction strategy. Instead of feeding the entire cleaned HTML to the model, we route the document through a specialized funnel that preserves only the essential structural components—specifically isolating the segment where navigation controls are physically rendered. This guarantees reliable detection of pagination while keeping processing efficient and robust.

In this pipeline, the raw HTML is ingested, stripped of noise, structurally collapsed to remove redundancy, and finally truncated to the bottom context before entering the Classification Engine. This approach ensures high-precision detection with minimal token usage, enabling significantly faster classification.

Detecting the navigation type is only half the battle; the agent must also respect the timing of the web. Many scrapers fail because they try to extract data before the browser has finished rendering content. To solve this, we implemented Iterative Scrolling, where the agent scrolls down the page periodically, in human-like increments, instead of jumping straight to the bottom. After each scroll, it monitors network activity and DOM changes, if the page loads new items or the DOM height increases, the agent recognizes a "Lazy Load" event and waits until the content stabilizes. This approach not only mimics human browsing behavior but also allows users to control scrape depth with simple parameters like max page, leaving the complex navigation logic entirely to the agent.

Once the agent successfully identifies the pagination type, whether Next Button, Infinite Scroll, or Load More, it transitions from understanding the structure to acting on it. Because the system already detects lazy-loading triggers through iterative scrolling, it can fully automate the entire navigation sequence, scrolling in controlled increments to surface hidden items, waiting for load contents, or clicking through paginated URLs as needed. Combined, these capabilities transform the agent from a passive classifier into an autonomous navigator, capable of reliably exploring the full breadth of diverse website structures without requiring the user to script a single rule.

When Patterns Lie

The biggest hurdle in automated scraping is "visual ambiguity," where elements look like navigation but serve a different purpose. Through our testing, we identified three critical edge cases that required specific engineering solutions to ensure high accuracy.

We used to have a problem where the “Next” arrow in carousels or product sliders was mistakenly detected as a real pagination button, but after switching to bottom-focus detection this issue was mostly solved. However, another problem appears on some websites such as liveinalabama.com, where the real “Next” button is hidden at the very bottom of the HTML, so our system cannot detect it immediately. Because of that, we try scrolling first with a timeout: if the page keeps scrolling until the timeout, we classify it as infinite scroll, but if the page stops scrolling before the timeout, we classify it with our engine and it works.

In addition, some platforms use what we call Fake Infinite Scroll, a user experience pattern where the page initially behaves like an infinite scroll, automatically loading content as the user scrolls. However, after a certain depth, the automatic loading stops to conserve backend resources, requiring the user to interact with a hidden or newly revealed control to continue. For instance, Tokopedia sometimes shows the first batch of products automatically, but deeper results require clicking a “Load More” button. A naïve infinite-scroll crawler would stall, endlessly waiting for content that never arrives. To handle this, our agent uses a timeout-driven approach, so if there is no new content appears after scrolling for a set duration, it triggers a full DOM re-scan to detect any fallback mechanism, such as a “Load More” button or hidden pagination link.

We also see a more intricate pattern called Hybrid Navigation, commonly used by large media platforms. These systems start by loading content via infinite scroll but then switch to a button-based model after a certain threshold. You can see this on YouTube, videos keep loading automatically, but at some point, a “Show More” button or footer element appears to reveal additional items. Instead of assuming navigation is fixed at page load, our agent continuously monitors the page. If scrolling stops yielding new items, it immediately checks the bottom of the page for buttons or hidden links that appear after the infinite-scroll phase. This allows smooth extraction even when the navigation model changes mid-session.

The Listing Agent shows that reliable extraction doesn’t come from fragile, per-site selectors or assuming a single interaction pattern. Each page is treated as a live environment to observe, probe, and classify. By combining human-like scrolling, click interactions, DOM normalization, behavioral checks, and domain-level caching, the agent achieves consistent and repeatable results across diverse site architectures while keeping operational costs predictable. This approach shifts the focus from brittle, site-specific engineering to a generalized, testable pipeline, which already demonstrates strong results in production and provides a solid foundation for further improvements.

Why Our Approach is Superior

We designed this architecture with careful research and engineering to create a truly general-purpose scraping agent. By studying common patterns across thousands of websites, and analyzing many representative ai scraping products and comparing their structures against common industry approaches we focused on building a system that is reliable, efficient, and adaptable.

1. Robust Pagination Detection Our agent prioritizes detecting pagination and navigation patterns, making it highly reliable across a wide range of websites—including Single Page Applications (SPAs) and dynamically loaded content. Unlike approaches that depend on simply following URLs to reach the next page, our agent can handle infinite scrolls, "Load More" buttons, and other modern web structures, ensuring that no data is overlooked.

2. Optimized Time and Cost Efficiency Our agent minimizes LLM usage by relying primarily on lightweight structural inference to determine the next steps, reserving heavier computation for only the most complex scenarios. This design significantly reduces processing time and cost while maintaining accuracy. By contrast, approaches that invoke Vision LLMs for every interaction may incur higher latency and cost due to overuse of computational resources.

What’s Next: Future Enhancements

Our next focus is pushing navigation-type detection and environment-behavior classification even further.

1. Vision-Language Models

We are currently refining the Listing Agent to handle the next generation of web complexity. As HTML structure becomes more obfuscated with Shadow DOMs and randomized class names, relying solely on code structure has its limits. We are experimenting with Vision-Language Models (VLMs) to allow the agent to "see" the page render, identifying navigation buttons based on visual placement and iconography rather than just code. Additionally, we are expanding our capabilities to detect and gracefully handle advanced bot protections and complex captchas that attempt to interrupt the navigation flow, ensuring longer-running scrapes remain uninterrupted.

2. Deeper Environment–Behavior Classification

We are building a more intelligent behavior-classification system that can understand how a page responds to user actions. The goal is to precisely categorize interaction behaviors, detect transitions between different interaction modes, and interpret dynamic rendering patterns—resulting in a faster, more accurate understanding of any website’s environment.