All posts by Matthew Conroy

From ranking to recommended: get your site ready to thrive in the age of AI agents

Post Syndicated from Matthew Conroy original https://blog.cloudflare.com/aeo/

Your next customer may not find you through a search engine. Instead, they'll ask an AI assistant: "how do I do X?"; "which option is best for someone like me?"; "just handle it for me" and an agent will find the answer, weigh the options, and act on their behalf. Increasingly, the moment that determines whether a customer chooses you happens inside a model's response — before a human ever sees your homepage.

This agentic audience is already here: by our count, fewer than half of all HTML page requests now come from a human. Not all of those machines are agents acting for a person, but that share is growing fast, and answer engines, shopping assistants, and research tools will shape which businesses are found and recommended. Discoverability used to mean ranking on a results page. Now it means being found, read, and confidently recommended by the agents that guide your customers.

The old metrics, human clicks and page views, no longer paint the full picture. We spent time talking to site owners who were staring at access logs full of AI bots, completely blind to whether those bots were capable of using their site or recommending their products and services to their users. We heard two main questions:

  • Can agents actually use my site?
  • Am I getting recommended?

To help site owners answer these questions, we have integrated our previous work on Agent Readiness into the Cloudflare dashboard, and added our new Answer Engine Optimization (AEO) tool as well. These tools treat agents as a core user base for your site, showing you how an agent will see it, and how often you get recommended.

The opportunity is big, and the bar is low, because most sites aren't built for this user yet. Just as early SEO rewarded the sites built for search engines, the sites built for agents will be rewarded now. The ones that are easy to find, read, and trust are the ones agents will recommend.

Diagnostics: is your site ready for agents?

Diagnostics is the technical checkup within Agent Readiness. It scans your site the way an agent reads it: it works out whether it's allowed in and whether it can discover your content, fetches a clean machine-readable copy, and finds the interfaces it can call. 

While a person just loads your homepage, an agent leans on your robots.txt, your sitemap, your response headers, a Markdown version of your content, and published metadata for authentication and tools.

Diagnostics runs those checks against a hostname and rolls the results into a single agent-readiness view, from "Not Ready" to fully agent-native. Every check comes back as pass, fail, or neutral, with a note on why it matters, and an evidence trail showing the exact request and response we saw.

The checks are grouped by effort, so you know where to start:

  • Quick wins: the high-impact basics most sites are missing, including a crawler-readable robots.txt, an XML sitemap, AI-crawler rules, and serving clean Markdown to agents
  • Technical groundwork: the next layer, including Content Signals that state how your content may be used, an API catalog, link headers, and agent login instructions
  • Advanced integration: the agent-native features, including OAuth discovery, MCP (Model Context Protocol) and A2A (Agent2Agent) agent cards, a skills index, Web Bot Auth, and WebMCP
  • Commerce: the emerging agent-payment standards including x402 (an extension of the classic HTTP 402 Payment Required status code), ACP (Agent Commerce Protocol), Universal Commerce Protocol (UCP), and AP2 (Agent Payments Protocol). This is informational for now, and not counted in your score.

Every suggested improvement comes with a next step. When there’s a Cloudflare feature that can help, there's a "Set up in Cloudflare" link straight to the setting, such as switching on Markdown for Agents or managed robots.txt. For everything else, there’s a "Copy Agent Prompt" button that proposes what your coding agent needs to build. Make the change, re-scan, and watch the checkmark turn green.

AEO: are AI assistants recommending you?

Diagnostics tells you whether agents can read your site. The AEO tab tells you what happens next: when a customer asks an AI assistant a question in your category, does it recommend you or a competitor? You can't look this up like a search ranking. There's no impression count and no missed-click report, so when a competitor gets named instead of you, the sale is gone and nothing tells you it happened.

We infer your industry (e.g. health and fitness) and category (e.g. sports apparel) from your site, and we probe the leading assistants (today, Anthropic's Claude and OpenAI's GPT) with likely customer prompts to see how they respond. We structure these prompts to mimic real-world discovery, asking for recommendations, product comparisons, and general advice within your category. By observing how models answer these realistic queries, you get metrics such as:

  • Citation Rate: the share of answers in your category that cite your site as a source
  • Prominence: when you are cited, how much of the answer is actually yours and how early it lands
  • Mention Rate: how often assistants name your brand in their answer — for example, how often "Cloudflare" shows up in the response, whether or not cloudflare.com is cited as a source. Read alongside your Citation Rate, it separates awareness from attribution: assistants naming you far more than they cite you means you're on their radar but not yet earning the citation — a specific, targetable gap.
  • Share of Voice: your slice of citations against those for your competitors, so you can see who is winning the prompts you're losing

To evaluate how an AI model perceives your market presence, we build a benchmark across each industry and category before scoring a specific site. We query AI assistants with likely prompts in that category — without specifying your brand — and record which sites are cited, where they appear, and how prominently they feature.

Rather than re-querying models every time a site owner runs a scan, we run this panel once per category and reuse the baseline across all accounts in that domain. Pre-computing this dataset provides three main benefits:

  • Zero latency: Results load instantly from a snapshot rather than waiting for live model queries.
  • Lower compute overhead: Aggregating queries at the category level avoids redundant AI calls across thousands of scans.
  • Industry Fit scoring: Reusing the panel corpus lets us map which brands consistently appear together, allowing us to derive an Industry Fit score that measures whether an AI assistant views your site alongside your actual competitors.

AI assistants rarely answer the same question the exact same way twice. To account for this variance, we use Cloudflare AI Gateway to prompt each assistant multiple times across different models. We then read the responses a customer would see — the answer text alongside the sources each assistant cited — and extract multiple signals from it. 

We evaluate not just whether your site was mentioned, but whether you were cited as a source, how early your citations appear in the answer, and how much of the final answer's substance is attributed to you. Where genuine judgment is required, Workers AI does the heavy lifting, running natively on our own infrastructure to read each reply and score how your citations and mentions appear. We also use exact text analysis rather than a model grading its own output. Together, this folds dozens of one-off replies into actionable metrics. By abstracting the multimodel query and evaluation pipeline, the tool provides metrics without requiring you to build your own evaluation framework.

Alongside the answers, an AI Operator Activity shows the real crawl and referral traffic on your site, per operator (OpenAI, Google, and so on): who reads your content, who sends visitors back, and the errors they hit on the way (403 blocked, 404 dead link). The pattern worth acting on is the operator that crawls thousands of your pages but refers no one, using your work without sending customers back.

Because these numbers are specific to your site, you can experiment, re-run the scan, and measure the impact on the exact questions that bring you business.

Meet your other audience

Until now, sizing up agents meant guesswork: grepping your logs to infer who visited, or feeding a chatbot a prompt and eyeballing whether it mentioned you. But with Agent Readiness and AEO, you can get the data you need to act. And because the requests actually pass through Cloudflare, these tools measure rather than estimate where possible, and will improve over time. 

Helping you see who's reaching your site and decide how to engage on your own terms is what we've always done. Agents are just the newest audience, and the businesses that make themselves easy for agents to find, understand, and trust are the ones that get recommended. Agent Readiness is where you find out whether you're one of them, and what to do if you're not yet.

Ready to find out if AI agents are sending customers your way? Head over to the Overview tab in your dashboard to get your site Agent Ready and request early access to AEO Visibility.

Building on the open, agent-ready web? Open the Agent Readiness tab in your Cloudflare dashboard and tell us what you're building on the Cloudflare Developer Discord.

Making AI search smarter

Post Syndicated from Matthew Conroy original https://blog.cloudflare.com/making-ai-search-smarter/

Search drives most experiences on the web. It’s how we get things done, and how nearly everything on the web gets found — the creators, the merchants, the answer to whatever you just typed into a box. For nearly 30 years, that discovery journey ran on a simple bargain: let a search engine crawl your content, and it sends you visitors. You turned those visitors into a business — through ads, subscriptions, or just the audience itself. Being discoverable and getting paid were the same thing. A year ago, on the first Content Independence Day, we drew a line to defend that bargain in the AI era. But a line in the sand was only a first step. Since then, the prevalence of AI search in consumers’ lives has only accelerated as more than 50% of traffic online is non-human. The threat is no longer a handful of training crawlers you can block; it’s search itself being rebuilt around AI answers.

Today’s answer engines read your page and hand the user a summary, so the visit — and the revenue that depended on it — isn’t needed. We see it firsthand, and independent research backs it up: a 2025 Pew Research Center study found that when Google shows an AI summary, users clicked on a traditional search result link just 8% of the time (about half as often as when there’s no summary) and clicked a link inside the summary only 1% of the time. That leaves our customers in a bind: opt out of AI and be hard to find, or opt in and deliver significant value to users while seeing increasingly little in return. Our customers want to be found and compensated for the value they provide, and right now they’re forced to choose.

Today, we’ve announced new bot options to help our customers better control who can access their site and what they can do with it. But blocking was only step one: saying “no” protects content without rebuilding the business models that sustain it. So, it’s time to start building the new economic model of the Internet, starting with search.

Rebuilding the bargain

Transparency and control are the foundation, but more is needed. In 2025, we laid out our foundation via a set of responsible AI bot principles: bots should be transparent about who they are and what they’re for, respect site owners’ choices, and act in good faith. Our tools hold bots to that bar. But enforcing good bot behavior doesn’t make AI search any better for the people relying on it, and it doesn’t send a dollar back to the creator whose work made the answer possible. We can do more than help the web say “no”; we can help rebuild what it says “yes” to.

So today, we’re announcing two initiatives that move from defense to offense and start putting both halves of that old bargain back together.

Make AI search smarter: By using the signals we see across our global network, like what’s fresh, what’s high quality, and what’s actually changed, we can help answer engines surface the most relevant content and reduce unwanted crawling. People searching get better answers, while costs are reduced for both AI companies and site owners if webpages are only recrawled when they’ve changed.

Pay creators for the value they provide: When your work is used to answer someone’s question, you should be rewarded instead of just being scraped for free. And you should be able to see what’s being used and what people are asking. This should be a real revenue stream, and an incentive to keep producing original content worth finding.

Making search smarter

Today we’re launching a research program to make AI search smarter and stop our customers footing the bill for crawls that produce nothing new.

More than 20% of the web sits behind Cloudflare’s network, which gives us a unique perspective. We can tell which pages have genuinely changed and which ones people and agents are flocking to. Through this program, we will explore using signals our customers have chosen to share about the freshness of their content, and we will combine those with our own insight into traffic flows, both human and bot. For answer engines, that’s a roadmap to high-quality content. For our customers, it provides a view of what users are actually asking, and how their content shows up in AI results. The aim is to measure two things: how much these signals help answer engines to surface fresher, higher-quality content, and how much unnecessary crawling they cut out.

That second benefit, cutting unnecessary crawling, is bigger than it sounds. Cloudflare data suggests that more than 50% of crawl traffic from good bots goes to re-fetching pages that haven’t changed — and that number is likely to climb as crawl volumes do. A signal that just says “nothing’s changed here” lets a crawler skip the trip. That saves the answer engine compute. More importantly, it saves site owners from serving and paying for requests they never needed to. 

The program is neutral by design: our goal is to make it work for every answer engine willing to play fair. It’s limited to search. We aren’t sharing any content, and nothing is used to train foundation models. We intend to publish what we learn, including the benefits to site owners such as better content discoverability and reduced server strain. We plan to make the capability broadly available later this year and reduce unnecessary crawling across our network.

From Pay Per Crawl to Pay Per Use

Last year we launched Pay Per Crawl so publishers could charge AI companies for crawling their content. It was a real start, but crawling is a crude measure of value. A single page might be crawled once and then cited in thousands of answers, or crawled over and over and never used at all. Creators want to be paid fairly for the value they provide.

So we’re starting to shape Pay Per Crawl into Pay Per Use. We’re running experiments with top AI companies, like Ceramic.ai and You.com, and the arrangement is straightforward: organizations can bring their payment models and easily scale them to content owners across the Cloudflare network.

Ceramic has built what it calls a “pay-per-query” model, so publishers who opt in can be paid when their content appears in Ceramic’s search results. This means payment is designed to follow the value the work delivers rather than the number of times a crawler happens to fetch it.

“To scale the future of AI search, we need a partner with massive reach and a shared commitment to transparency and fair compensation,” says Anna Patterson, founder and CEO of Ceramic.ai. “Cloudflare allows us to easily and programmatically scale our operations. By bringing our pay-per-query model to their network, we ensure millions of content owners can seamlessly opt in to be compensated every single time their content appears in our search results.”

In addition to compensation, content owners participating in the Cloudflare/Ceramic program will unlock new reporting to help with answer engine optimization (AEO). Customers can finally see the top queries leading to their content appearing in search results, the specific webpage and snippet, their average search result ranking position, and more. This is the first of many products we’ll be launching to help our customers with discoverability.

This is just one emerging approach. Another comes from You.com: agents can pay on demand for a specific piece of premium content they need, without any upfront commitment. New payment models from AI providers are being tested (e.g., Pay per Query, Pay per Result, etc.) and we have the infrastructure to support them all. 

We want to be honest that this is an experiment. There’s a lot to learn, including exactly how this holds up at the scale of the Internet. We’ll work that out with our partners and our customers as we go, and share what we learn. But the goal is clear: AI search companies get fresher, better-grounded answers, and the customers whose work makes the answers possible get paid when they help. Cloudflare’s job in all of this is to provide the infrastructure layer that makes this market flourish. 

We think this is a more natural fit for where the economics of search are heading. The old, human web optimized search to save time — providing excerpts, ten blue links, and a click. The agentic Internet is different: an agent can read fast and search continuously. Search is becoming something an agent does dozens of times to answer a single question, closer to a utility than a destination. In that world, the unit that matters isn’t the crawl or the click. It’s the outcome. Pricing the outcome, and paying the people who made it possible, is how the web continues to thrive.

The headline we want to earn

A year ago on Content Independence Day, the headline was a default ‘no’: AI can’t crawl without compensation. This year, our focus is on giving our users more products and controls to say ‘yes’ and bring more benefits with it.

Today’s announcements are just the beginning. Cloudflare’s research project is designed to see if our signals produce better results with less crawling. Pay Per Use is a promising direction we’ll experiment with alongside partners who believe that content creators deserve fair compensation for their work. This is how the last 30 years of the web got built too: somebody runs the pilot that turns “the model is broken” into “here’s the new model,” one experiment at a time. We believe there’s value to our customers to be discoverable in this new agentic era, and to optimize their content for maximum discovery. But they should be able to do this without giving away their most valuable creative assets for free.

The web is changing, and the business models it’s relied on are changing with it. The old Internet was open, neutral, and worth contributing to. We have a rare chance to keep it that way, and to build the business models that fund it in the future. Smarter answers for humans and agents asking the questions. A fair deal for the people whose skill, creativity, and commitment makes the answers worthwhile. That’s how we pursue Cloudflare’s mission: to help build a better Internet.

Happy Content Independence Day!

Building on the open, agent-ready web? If you are interested in learning more about the Ceramic and You programs, please fill out this form. If you’re building an answer engine and want to crawl smarter, we’d love to hear from you too: [email protected].