AI agents require more than just reasoning skills; they also need to actively utilize the web.

      A company introduces an AI customer service assistant that is equipped with an adequate and up-to-date model for the task. The assistant goes live, but within a week, the number of support tickets is worsening rather than improving.

      The issue lies not with the model but with the company’s website. The assistant needs to refer to the return policy found in a PDF. The shipping calculator it is supposed to use requires a multi-step form. The product specifications it should access are located behind a tabbed interface that only activates after a click. While the site functions well for human visitors, it presents challenges for the AI, rendering portions of the site inaccessible.

      This obstacle is a common issue faced by many deployments of agentic AI, which is not primarily related to the model.

      According to McKinsey’s 2025 State of AI report, 23% of organizations are currently scaling agentic AI systems in at least one aspect of their business, with an additional 39% in the experimentation phase. Many of these implementations will encounter the same barrier: a website designed for human interaction that must be navigated by software requiring data in a format that humans have never needed. The next major advancement for AI agents is not enhanced reasoning abilities but rather the capacity to effectively browse and utilize the live internet.

      The essential tasks for an AI agent on the web can be summarized in three main functions, all of which must be performed effectively for the agent to be valuable in operational settings.

      1. **Search**: The agent must locate the correct information, not merely URLs leading to links but the actual content that it can analyze. For instance, if a customer queries an insurance chatbot about their policy's coverage for a specific incident, the agent must retrieve the relevant policy section rather than just a page of search results.

      2. **Scrape**: After locating the page, the agent needs to extract the content accurately. Most contemporary websites complicate this process, as pages often load through JavaScript, with content hidden within expandable accordions, tabs, or lazy-loaded segments. The HTML format received by the agent frequently differs significantly from what a human perceives in their browser.

      3. **Interact**: This is where many agent demonstrations falter in real-world applications. Much of the information that matters to humans is not simply available via a URL. Instead, it can be found behind buttons prompting "load more," search fields, multi-step forms, navigation bars, or login requirements. A scraper that only processes static pages is unable to access these resources, whereas an agent capable of interaction (clicking, navigating, filling out forms, submitting information) can successfully retrieve this data. The ability to interact distinguishes whether the AI can fulfill its intended role.

      Among these tasks, interaction presents the most recent and challenging development. Additionally, this is where the most valuable applications of AI agents exist: shopping assistants that compare prices across different sites, research tools aggregating data from interactive dashboards, and customer support bots navigating documentation portals like actual users.

      Firecrawl serves as a foundational layer for this functionality.

      Firecrawl is one company focused on building the infrastructure necessary to support all three capabilities. Their platform acts as a bridge between AI agents and the live web, managing search, scraping, and interaction via a single API. Their open-source project has received over 120,000 stars on GitHub. Notable customers such as Lovable, Replit, and Zapier integrate Firecrawl into their operations. In 2025, Nexus Venture Partners led a $14.5 million Series A funding round for the company, with Shopify CEO Tobi Lütke coming on board as an investor after using Firecrawl as a customer.

      The proposition is clear: an AI agent utilizing Firecrawl does not require its development team to write customized code for every website it interacts with. Instead, it calls an API, which simplifies much of the essential technical tasks: rendering JavaScript, navigating dynamic pages, interacting with website components, and returning organized output that AI systems can utilize.

      "Every AI company needed clean web data, and no one was addressing it effectively," stated Eric Ciarla, a co-founder of Firecrawl. "So we built Firecrawl."

      Ciarla and his co-founders encountered these issues directly while establishing their previous venture, Mendable, an AI search platform serving various organizations. While the search product was effective, the underlying infrastructure for extracting data from each customer’s website failed. Each new integration required reconstructing fragile extraction code that would often break with any change to the customer's site. Mendable's experience was not unique; many AI companies integrating web data faced similar hurdles, repetitively rebuilding internal extraction tools.

      There is also a transformation occurring alongside this technical evolution, altering the dynamics for businesses that haven't yet considered how AI agents access their websites.

      For the past two decades, the transition from “a customer is searching for something” to “a customer discovers your business” predominantly involved traditional search engines. However, AI assistants