Web Data for AI Agents

The recent AI/LLM revolution was only made possible thanks to web data. All major generative AI models have been trained using data scraped from the web.

While models like GPT or Llama are very powerful, on their own they have a very limited knowledge of the current world, and will happily hallucinate things they don’t know. To make these models useful for practical applications, they need to be supplied with up-to-date context, and wrapped into specialized units that we call AI agents. And the best place to find such data? Yet again, the web.

AI Agents Don’t Work In a Vacuum

People often assume that AI “knows everything.” But that’s not really true. Language models like GPT are trained on huge datasets collected from the web, but those datasets represent a snapshot in time. GPT 4, for instance, doesn’t know anything that happened after early 2023 unless it’s been explicitly updated with that information.

Even worse, its knowledge is often fuzzy. A neural network is, at its core, a lossy compression of the data it was trained on. It can make very clever guesses, but it’s still guessing. That’s why these models sometimes hallucinate facts, misattribute quotes, or present outdated information as current.

This is where agents make a difference. Unlike static models or simple LLM apps, agents can actively look for new information online, retrieve it in real time, and use it to augment the models with the knowledge to provide accurate responses. To do this effectively, they need access to a living, breathing source of truth - and that’s still the web.

The Web Is Fuel for Modern AI

Apify has spent years building tools and infrastructure for web scraping and crawling. Long before LLMs hit the mainstream, many companies were already working with systems that gathered real-time data from thousands of websites. Now that AI agents are becoming more common, the value of that data is even clearer.

Agents like Deep Research or Manus can crawl public sources, analyze large volumes of content, and synthesize meaningful insights in minutes. What might take a team of analysts an entire week, they can now do in the time it takes to run a script.

For example, let’s say you want to identify trends across your industry, track competitor activity, or monitor product pricing across multiple marketplaces. Instead of manually checking websites or pulling reports, an agent can browse, extract, and summarize what’s happening, then deliver a clean, structured overview you can act on.

But for any of this to work, AI agents need access to data .

Websites Don’t Love Bots

Most websites are not thrilled about automated access. Many now use a combination of rate limiting, bot detection, and CAPTCHAs to block automated crawlers and bots. This is understandable - they need to protect their infrastructure and ensure a fair experience for their users.

That said, AI agents can’t deliver value unless they can see what’s on the page. Without being able to read the web pages by bots, there would be no Google, Bing, Perplexity, and the entire AI revolution!

That’s where proxies come in.

Proxies allow agents to route their traffic through different IP addresses, appearing as human visitors rather than bots. This helps avoid blocks and ensures the agent gets access to the actual content.

It’s not just about avoiding detection, it’s also about context. Websites often display different content depending on a visitor’s location. Prices, search results, and even availability can change between countries or cities. Proxies became a necessary tool for any web scraping , albeit no longer sufficient.

Doing It the Right Way

Of course, just because something is technically possible doesn’t mean it’s always the right thing to do. Scraping data for AI agents should be done with care, both legally and ethically.

Generally speaking, scraping public web data is typically allowed, as long as it’s not behind authentication or paywalls, and it doesn’t contain personal information (this is not legal advice). But there’s also an ethical layer. Just because you can scrape doesn’t mean you should scrape indiscriminately. That means:

• Don’t overload websites with too many requests
• Respect the robots.txt file and reasonable rate limits
• Avoid collecting personal or sensitive information
• Honor clear “no scraping” signals when they exist

The goal isn’t to break the web, it’s to work with it in a sustainable way. Done properly, web scraping can coexist with website operations, benefiting both users and data consumers.

The Demand for Web Data Is Only Going to Grow

We’re still early in the evolution of AI agents. Most tools today are basic wrappers around LLMs with some prompt engineering. But we’re starting to see agents that can plan tasks, break them down, loop through information, and even decide when to stop or ask for help.

As these agents become more capable, they’ll need more than static memory - they’ll need live input. And for that, the web is unmatched in both scale and freshness.

Web data isn’t just raw text. It’s live market trends, product reviews, pricing intelligence, policy updates, academic papers, job postings, and social signals. It’s real-time, real-world context. No other data source combines that breadth and recency.

This shift means web data will go from being a useful input to a critical foundation for AI. The businesses that prepare for that now, by building or partnering with infrastructure that provides reliable, ethical access to web data, will be the ones that thrive in the agent economy.

Final Thoughts

Web data is the foundation for AI agents. Without it, they’re cut off from the world, stuck with whatever stale knowledge they were trained on. With it, they become powerful, real-time problem-solvers that can act on the latest information, adapt to change, and deliver real value.

But good data doesn’t come automatically. It requires infrastructure like proxies, smart scrapers, and data pipelines, and it requires discipline to do it in a way that’s sustainable and fair.

If you’re building AI agents, don’t overlook where their knowledge comes from. The web is your best source of up-to-date, high-signal information. But you need the right tools - and the right mindset - to access it.