With Gartner forecasting that 40% of enterprise applications will embed task-specific AI agents by 2026, businesses are rapidly moving from simple, prompt-based chatbots to autonomous systems that act independently. Yet as these AI agents and workflows become embedded across more business functions, much of the conversation has focused on the capabilities of the models themselves, how well they can reason, how quickly they can complete tasks, and what businesses can achieve with them.
But there is a more fundamental question that is often overlooked. What web information are these systems actually working with?
An AI agent can only make decisions based on the information available to it. If that information is stale, incomplete or inaccurate, those shortcomings can feed directly into its outputs and actions. As AI moves beyond generating responses and towards carrying out tasks on behalf of businesses, the quality of the data it can access becomes increasingly important.
The web is one of the richest sources of real-time information available to AI systems, but accessing that information reliably is not straightforward. Websites change constantly, information becomes outdated, and data can be difficult for automated systems to access and use effectively.
How limited data exacerbates errors
Facing pressure to innovate, companies can fall into the trap of adopting the latest tool without ensuring the foundations for its effective use are in place. As every user of a large language model will know, AI already has a hallucination problem, where it reports back information that seems plausible but is actually false. The risk of wrong answers grows when a model relies only on training data that quickly goes out of date, or on live data drawn from a narrow range of lower-quality sites. Grounding it in accurate, live web data from a wider variety of sources reduces that risk, which is why it is becoming a critical part of the AI stack.
If you’re using AI agents that can take actions autonomously, if they can only access certain information, they will always be operating in a more constrained and error-prone environment. Stale or inaccurate web data, for example, can feed errors into AI agents and workflows. The AI agent might pull the pricing for a product from an article that is a year old rather than the live prices that are held on a protected shopping site (which it isn’t able to access). So, the smaller and less diverse the data pipeline is, the more likely there will be inaccuracies or bias in it.
Barriers to data access
As a result, how well an AI model can pull information from the web plays a large part in determining the quality of its outputs. That means the volume and variety of sites it can access and the relevance and accuracy of the information it pulls from those sites. To get that range and scale of data, AI agents need reliable, automated access to the web. Yet this is where web scraping tools can encounter obstacles to websites that impact their ability to do this, especially when they’re faced with heavily-protected sites.
A ‘successful’ scraping request, for instance, can mean the server of the website returns a response to the scraping tool with the webpage’s code (which is then used to extract the relevant data from). But if that page is a CAPTCHA challenge, the code contains none of the data the tool is looking for. Similarly, the tool might get blocked by sophisticated bot detection software, which various sites have, or fail on a JavaScript-heavy site because it can’t render the page, so the data never loads.
These obstacles mean AI agents relying on basic web access tools will be missing out on a range of relevant data that is vital to the quality and accuracy of their outputs and actions.
Creating access
The latest web scraping APIs, however, use a blend of statistical analysis and machine learning models to bypass anti-bot measures and protected sites without overwhelming the server. They can access and organise thousands, even millions, of data points from websites to enable vast and varied data collection at a high success rate. These APIs can be connected to AI agents as tools, giving them reliable access to the pages they need, including protected and JavaScript-heavy sites.
And it’s not only about access. It’s whether the tool checks that a page actually contains the data, and what it hands the agent. Clean text or markdown is a start, but the bigger gain comes from structured fields, such as a product’s price, title or availability, that an agent can reason over directly instead of untangling a raw page, and that stay consistent when a site changes its layout. That improves accuracy, saves time on cleaning and uses fewer tokens. Just as important, when a page can’t be reached, the agent should get a clear error rather than an empty result it might mistake for real data.
This combination of reach, access and usability gives AI agents a steady flow of fresh data, collected as often as the task needs, from thousands of valuable sources and data types.
Prioritising quality, reliable sources
If agents are missing out on a range of up-to-date, relevant data when they are taking actions, they are not going to deliver the results companies are looking for, and can even become a risk. That’s why the ability to access real-time data from across the web has become so important – and why companies need to be considering the quality and reliability of their data sources alongside their AI model.
By building on reliable web data infrastructure that collects high-quality data from a range of sources, including harder-to-reach sites, teams can enhance the accuracy and relevance of the data their AI agents can access. In turn, this means agents can uncover more detailed insights and patterns within the data and work much more effectively. And the better web insights they can gather, the greater impact they will have on the business.
