If you're a developer who has ever worked on web automation, you know the pain. You spend hours crafting the perfect set of CSS selectors or XPath queries to navigate a website and extract data. Your script works flawlessly. Then, a week later, it all comes crashing down. A frontend developer changed a class name, restructured a div, or tweaked the site's layout, and your once-brilliant automation script is now completely broken.
This cycle of building, breaking, and fixing is the fragile reality of traditional web automation. We've been tethered to the underlying structure of a webpage, making our tools susceptible to the smallest cosmetic changes.
But what if we could build automation that understands a website's intent, not just its structure? What if we could simply tell a tool what we want to achieve in plain English? This isn't a futuristic dream; it's the new reality powered by AI agents. Welcome to the future of web automation.
For years, tools like Selenium, Puppeteer, and Playwright have been the pillars of browser automation. They are incredibly powerful, giving us fine-grained control over a headless browser. However, this power comes at a cost: complexity and fragility.
The core logic of these tools relies on finding specific elements on a page using selectors:
Anyone who has written these knows the problems:
We've been telling the computer how to click every button and find every field. It's time for a smarter approach.
Imagine replacing complex selector logic with a simple, human-readable objective. That's the core principle behind browse.do. We've built an AI agent that uses a full, headless browser to understand and interact with any website on your behalf.
Instead of writing fragile code to find elements, you just describe your goal. The AI agent handles the rest.
Consider this example of finding the top story on Hacker News:
import { browse } from "@do-inc/agents";
async function getTopHackerNewsStory() {
const result = await browse.do({
url: "https://news.ycombinator.com",
objective: "Find the title of the top story and its URL."
});
console.log(result.data);
// Expected output:
// {
// "title": "The title of the top story here",
// "url": "https://the-story-url.com"
// }
return result.data;
}
getTopHackerNewsStory();
Notice the objective. We didn't specify any selectors, element IDs, or classes. We stated what we wanted in plain English, and the agent intelligently identified the relevant elements, extracted the data, and returned it in a clean, structured JSON format.
Traditional web scraping tools are blind. They see a document of HTML tags and follow your selector-based instructions precisely. If the structure changes, they fail.
An AI agent, like the one powering browse.do, sees the website more like a human does.
This approach makes your automation an order of magnitude more resilient. If a developer changes a button from a <button> tag to a <div> with a role="button", a selector-based script breaks. The AI agent, however, understands its function and can still interact with it successfully.
This shift from imperative commands to declarative objectives unlocks powerful new possibilities for robotic process automation (RPA) and data extraction.
The evolution of technology consistently moves toward higher levels of abstraction. We went from assembly language to compiled languages, from managing our own servers to serverless cloud functions. Web automation is undergoing the same transformation.
The era of writing brittle, selector-based scripts is coming to an end. The future belongs to intelligent agents that we can instruct, not micromanage. By focusing on what we want to achieve, we can build more powerful, resilient, and maintainable automation than ever before.
Ready to leave fragile selectors behind? Get started with browse.do and turn your most complex web automation workflows into simple function calls.