The work breaks down into three jobs, and all three have to work for an agent to be useful in production.

Search. The agent needs to find the right information. Not URLs to a list of links, but actual content it can read and reason over. If a customer asks an insurance chatbot whether their policy covers a specific event, the agent needs to surface the relevant section of the policy, not a search results page.

Scrape. Once the agent finds the page, it needs to read it cleanly. Most modern websites don’t make this easy. Pages load through JavaScript that has to execute first. Content lives inside expandable accordions, tabs, and lazy-loaded sections. The HTML the agent receives often looks nothing like what a human sees in their browser.

Interact. This is where most agent demos fall apart in production. A lot of the information humans care about isn’t on a simple URL. It’s behind a “load more” button, a search box, a multi-step form, a navigation menu, or a login. A scraper that can only read static pages can’t reach any of it. An agent that can interact (click, navigate, fill, submit) can. The difference between the two determines whether the AI can actually do its job.

Of the three, interaction is the newest and the hardest. It’s also where the most useful agent applications live: shopping assistants that compare prices across sites, research tools that pull data from interactive dashboards, customer support bots that navigate documentation portals the way a real user would.

There’s a shift happening alongside the technical one, and it changes the stakes for businesses that haven’t thought about AI agents reading their websites yet.

For two decades, the path from “a customer is looking for something” to “a customer finds your business” often ran through traditional search engines. AI assistants are increasingly where people start when they want a recommendation, a comparison, or an answer. The AI assistant goes off, pulls information from the relevant websites on the person’s behalf, and comes back with a synthesized answer. If the AI couldn’t parse your site, your business doesn’t appear in the answer.

Ciarla argues this changes how businesses should think about AI crawlers entirely. “Behind every AI agent is a human trying to find something,” he says. The dominant industry framing has treated AI crawlers as unwelcome automation: bots to defend against, traffic that drains server resources without sending human visitors in return. That framing made sense when the only things reading websites at scale were search engines indexing for human visitors later. It makes less sense when AI agents are the path the human is using to find.

 In Ciarla’s view, blocking AI crawlers today may be comparable to limiting visibility on an emerging discovery channel. He argues that doing so could reduce opportunities for businesses to be found through evolving customer search behaviors.

What makes Firecrawl’s position in this shift unusual is that it doesn’t require businesses to do anything. Most approaches to AI visibility put the work on the site owner: add new markup, expose new endpoints, restructure pages, learn a new optimization discipline on top of the existing SEO one. Firecrawl works from the opposite direction. The platform handles the conversion between human-readable site and machine-readable data automatically, in real time. A business never needs to know AI agents are reading the page. The agents get what they need anyway.

As agents pull more information from more sites, the relationship between AI systems and the sources they depend on becomes a real question. A model where AI extracts value from web content without anything flowing back to the people who created it isn’t durable. Publishers are pushing back through lawsuits and access blocks, and major sites are increasingly walling off their content from AI crawlers entirely. The underlying ecosystem isn’t healthy, and the long-term cost lands somewhere eventually.

If you’re building with AI, the practical takeaway is simple. The model is no longer the differentiator. Almost everyone has access to the same frontier models, and the gaps between them keep closing. What separates an AI product that works in production from one that doesn’t is increasingly the layer underneath, and whether the system can actually reach the information it needs to be useful. Investing in that layer may offer meaningful engineering benefits.

If you’re running a business and you’ve never thought about AI agents reading your website, that’s the moment to start. The discovery channel is shifting. A customer who previously may have found a business through a traditional search engine may now use an AI assistant as part of the discovery process. If that assistant can’t read your site, they may not find you at all. Many businesses continue to optimize primarily for human readers and search engines while evaluating how AI-driven discovery may affect their digital presence.

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