Is your site ready
for AI agents?

AI agent readiness is a measure of how well your website can be discovered, understood, and acted upon by AI systems like ChatGPT, Claude, Perplexity, Google Gemini, and other large language models (LLMs). It goes beyond traditional SEO - instead of optimizing for search engine rankings, agent readiness focuses on structured data (Schema.org), machine-readable protocols (llms.txt, robots.txt), semantic HTML, and API discoverability. As AI-powered search replaces traditional blue links, agent readiness determines whether your business shows up when someone asks an AI assistant for a recommendation.

AI agents are rapidly changing how people find and interact with businesses online. Over 60% of Google searches already end without a click, and AI Overviews are reducing organic traffic to top-ranked sites by up to 79%. When someone asks ChatGPT to "find a good project management tool" or asks Claude to "compare CRM platforms," the AI reads your structured data directly - it does not browse your site like a human. If your website lacks proper Schema.org markup, meta tags, and machine-readable content, AI agents simply cannot recommend your business, show your products, or answer questions about your services. This is the new battleground for online visibility.

AgentReady analyzes over 50 technical signals across three layers. The Discovery layer checks whether AI agents can find you - we evaluate your robots.txt permissions for AI crawlers (GPTBot, ClaudeBot, Google-Extended), XML sitemap presence and coverage, llms.txt file, and JavaScript rendering compatibility. The Data Quality layer checks whether agents can understand you - we analyze Schema.org JSON-LD markup presence, validity, and completeness, meta tag quality, OpenGraph tags, alt text coverage, and content structure. The Actionability (Advanced) layer checks whether agents can act on your behalf - we look for MCP (Model Context Protocol) endpoints, API discoverability via Link headers and SearchAction schema, and structured navigation. Most sites do not support MCP today, so this layer is treated as advanced readiness. You get a score from 0 to 100 with prioritized fix recommendations and code examples.

Yes, completely free. Enter any website URL and get a full AI readiness report in about 30 seconds - no signup, no email, no credit card. You'll see your overall score, a breakdown across all three layers (Discovery, Data Quality, Actionability (Advanced)), individual check results with pass/fail/warn/info status, a competitor comparison if you add competitor URLs, and prioritized fix suggestions with ready-to-use code examples. We believe every website owner should be able to check their AI agent readiness without barriers.

llms.txt is an emerging web standard proposed by Jeremy Howard that works like robots.txt, but for large language models. It's a Markdown-formatted file hosted at your domain root (/llms.txt) that tells AI systems what your site is about, what content matters most, and how it's structured. Think of it as a table of contents that helps LLMs efficiently understand your site without crawling every page. While no major LLM provider officially supports llms.txt yet, adoption is growing rapidly - and since implementation takes just a few minutes, it's a low-effort, high-potential-upside improvement. AgentReady checks whether your site has one and whether it's properly formatted.

Traditional SEO audits focus on Google rankings - keyword optimization, backlink profiles, page speed, Core Web Vitals, and mobile-friendliness. An AI agent readiness audit evaluates a completely different set of signals. We check whether AI crawlers like GPTBot and ClaudeBot are allowed in your robots.txt, whether your Schema.org structured data is valid and complete enough for machines to extract product info or business details, whether you have an llms.txt file, whether your content is accessible without JavaScript rendering, and whether you expose MCP endpoints or API discovery mechanisms. The overlap with SEO is limited to meta tags and Schema.org - everything else is unique to agent readiness. As AI-mediated search grows, you need both.

Generative Engine Optimization (GEO) is the practice of optimizing your online presence for AI-powered search and discovery - tools like ChatGPT, Claude, Perplexity, and Google AI Overviews. While traditional SEO optimizes for ranked lists of links, GEO optimizes for the answers, summaries, and recommendations that AI systems generate. Key GEO strategies include implementing comprehensive Schema.org markup, ensuring clean semantic HTML, maintaining an llms.txt file, allowing AI crawlers in robots.txt, and making your content citable with clear facts and structured data. AgentReady is a GEO audit tool - it tells you exactly how well your site performs across these signals.

Start with the highest-impact fixes AgentReady recommends. Typically, the biggest wins are: adding JSON-LD Schema.org markup to your pages (Product, Organization, Article, FAQPage types), ensuring your robots.txt allows major AI crawlers (GPTBot, ClaudeBot, Google-Extended, PerplexityBot), creating an XML sitemap and referencing it in robots.txt, adding an llms.txt file at your domain root, completing your meta tags (title, description, canonical) and OpenGraph tags on every page, and adding descriptive alt text to images. Each fix AgentReady suggests comes with a code example you can copy-paste. Most sites can jump 20-30 points by implementing the top 3-5 recommendations.

We check your robots.txt for permissions across all major AI crawler user agents: GPTBot and ChatGPT-User (OpenAI), ClaudeBot and Claude-Web (Anthropic), Google-Extended (Google Gemini), Bingbot (Microsoft Copilot), PerplexityBot (Perplexity AI), Applebot-Extended (Apple Intelligence), Bytespider (ByteDance AI), CCBot (Common Crawl, used by many LLM training sets), FacebookBot (Meta AI), and Amazonbot (Amazon Alexa AI). We tell you which ones are allowed and which are blocked, so you can make informed decisions about your AI crawler policy.

Yes - structured data is one of the strongest signals for AI visibility. A benchmark study by Data.world found that LLMs grounded in knowledge graphs built from structured data achieve 300% higher accuracy compared to those relying on unstructured text alone. Content with proper Schema.org markup has a 2.5x higher chance of appearing in AI-generated answers. In 2026, Schema.org has evolved from an SEO enhancement into critical infrastructure for the agentic web - it's how AI agents know what your business is, what you offer, and how your content should be understood. Microsoft's NLWeb initiative, built on Schema.org, is specifically designed to enable conversational AI interfaces over structured web data.