I’ve been dealing with this for a few months now and it’s become a real pain point. We’re scraping a bunch of webkit-rendered pages that load content dynamically as you scroll, and simple automation just falls flat. The page renders, but half the data isn’t there yet because it’s waiting for async calls to finish.
I tried the usual stuff—waiting for specific elements, adding delays—but it’s brittle and slow. Got me thinking about whether there’s a better approach than just hammering the page with waits.
Recently I started experimenting with having multiple agents work together on this. One agent handles the initial page load, another monitors for new content appearing, and a third validates what got pulled. It’s still early, but it seems less fragile than a single monolithic automation.
The thing is, setting this up usually means writing a lot of custom code or maintaining a bunch of separate tools. I’m curious how other people are tackling this without burning hours on infrastructure.
What’s your actual workflow when you hit pages that load content in chunks? Are you doing something similar with multiple parallel processes, or is there a pattern I’m missing?
This is exactly where orchestrating multiple agents shines. Instead of fighting async rendering with waits and timeouts, you can have one agent handle the page load while another monitors for content changes and a third validates what appears.
The real win is that you describe what you want in plain language and let the AI Copilot generate the workflow. So instead of writing custom code for each lazy-loading scenario, you just say something like “load the page, wait for images to appear, extract text from each section, validate the data” and it builds the automation for you.
Multiple agent coordination handles the complexity of async content without you managing callbacks or writing middleware. And if a site changes how it loads content, you regenerate the workflow instead of debugging code.
You can test this approach across different AI models too, which matters because some handle timing and validation better than others. A single subscription gives you access to all of them, so you can experiment without juggling API keys.
Start with a template focused on webkit scraping, then customize it for your specific pages. That gets you running in days instead of weeks.
I’ve hit this same wall. The key thing I learned is that you can’t just wait for one selector. Lazy loading means content appears in waves, sometimes over minutes.
What actually worked for us was breaking the scraping into phases. Phase one gets the initial viewport, phase two scrolls and waits for new content to appear, phase three extracts from the newly loaded sections. We insert small validation checks between each phase to make sure we got what we expected before moving on.
It’s less about fighting the async timing and more about building a process that expects content to arrive gradually. The validation between phases prevents corrupted extracts from propagating downstream.
Lazy loading on webkit pages is frustrating because the traditional approach of waiting for selectors doesn’t work well. The real issue is that you’re often waiting for network calls to complete, not just DOM changes.
Consider breaking your automation into observable states rather than time-based waits. Monitor network activity or use mutation observers to detect when new content actually appears. This approach is more resilient because it responds to real changes instead of guessing how long to wait.
Also, consider running your scraper in parallel for different page sections if possible. This reduces the total time spent waiting for content and lets you gather data more efficiently.
The scalability issue with lazy-loaded content typically surfaces when you’re managing state across multiple page loads and rendering cycles. Your instinct about parallel agents is sound, but the complexity lies in coordination and idempotency.
Ensure each agent has clear responsibility boundaries. One handles viewport management, another handles content detection, another handles extraction. This prevents race conditions where multiple agents try to process the same content.
Implement checkpoint validation. After each phase, verify the extracted data meets expected criteria before proceeding. This catches rendering failures early and allows graceful retry logic.
Use IntersectionObserver instead of waits. It detects when elements enter the viewport, wch is way more reliable for lazy loading. Pair it with network monitoring to catch async calls finishing.