Prepare your site for AI agents that browse, compare, and buy. Audit agent accessibility, analyze data extractability, and build a structured data roadmap for the next 12 months.
AI agents don't just answer questions anymore. They can browse websites, compare pricing tables, extract feature lists, and complete transactions. OpenAI's Operator, Google's Mariner, and Anthropic's computer use capabilities are turning AI from answer engines into action engines. Content optimized for human readers, or even for chatbot citation, may be completely unusable by AI agents that need structured, machine-readable data. A pricing page that requires JavaScript interaction to reveal plans is invisible to an AI agent. A feature comparison buried in marketing copy is unextractable. This playbook uses Claude + Qwairy's MCP server to audit whether your site is ready for AI agents that browse, compare, and buy, and build a readiness plan for the next 12 months.
💡 TL;DR Connect Qwairy MCP to Claude Desktop, paste the complete prompt from the bottom of this article, and get an agentic search readiness audit with structured data recommendations in under 15 minutes. Scroll to "The Complete Prompt" to skip straight to the copy-paste version.
Traditional AI search reads your content and cites it in answers. Agentic search navigates your site and takes actions. The requirements are fundamentally different. For citation-based AI search, the content needs to be well-written, authoritative, and accessible to crawlers. For agentic AI search, the content needs to be machine-readable, structured, and action-oriented. A comparison table with clear columns, a pricing page with structured data, an API with documentation - these are what AI agents need. The shift is happening now. AI agent requests have reached significant volumes relative to human search, and the trajectory is exponential. Brands that optimize for AI agents today will have a structural advantage as agent-driven browsing becomes a primary channel.
By the end of the workflow:
A technical readiness audit. Whether AI agents can access and navigate your site (crawlability, rendering, structured data).
A page-level agent accessibility map. Which pages are agent-friendly (structured, machine-readable) and which aren't.
A structured data gap analysis. Missing schema markup, pricing data, feature tables, and API documentation that agents need.
An agent optimization roadmap. Specific technical changes to make your site agent-ready, prioritized by expected traffic impact.
Qwairy account with active monitoring (Growth plan or above for MCP access).
Claude Desktop with Qwairy MCP connected.
Claude Pro or Max subscription for extended conversations.
What to ask Claude:
Run a technical status check on my site. Show me the robots.txt analysis for AI agents, llms.txt status, and any page issues that would prevent AI agents from browsing my site. Also pull page presence to see which pages agents can currently access and cite.
Tools Claude uses from Qwairy’s MCP: get_technical_status then get_page_presence
The technical status reveals whether AI agents can physically access your pages. Page presence shows which pages are already being discovered and cited.
The reasoning behind this step: Agent accessibility is the foundation. If AI agents can't render your pricing page (JavaScript-dependent), can't access your feature comparison (blocked by robots.txt), or can't find your API docs (not in sitemap), no amount of content optimization helps.
What to look for:
Key pages for agent browsing are pricing pages, feature comparison pages, product documentation, API references, and help/support content. These are the pages agents visit when comparing options or completing tasks for users. Check whether each of these exists in page presence and whether they have any technical issues.
Pay special attention to two patterns. First, pages with NEEDS_ATTENTION status that cover pricing or features. These are the pages agents need most, and any access issue means lost conversions. Second, pages with zero citations despite being in the sitemap. For JavaScript-heavy sites, this almost always means the page renders as an empty shell for AI crawlers. You can verify by asking Claude to also run get_brand_performance to see if the overall source citation rate is lower than expected given your content volume.
Run a free audit: see if ChatGPT, Gemini and Copilot recommend you, in about a minute.
What to ask Claude:
Pull my source URLs filtered to my own domain. Show me which of my pages AI engines currently cite and what content they extract. For the shopping insights, show me what product data AI engines can see. I want to understand what's extractable today vs what's hidden.
Tools Claude uses from Qwairy’MCP: get_source_urls filtered to own domain, then get_shopping_insights, then get_source_domains with isSelf=true, then get_keyword_triggers
Source URLs shows what AI engines currently extract from your site. Shopping insights reveals what product data is visible to AI. Source domains with the self-filter shows aggregate citation stats for your domain. Keyword triggers reveals which product attributes and features AI engines associate with your brand, indicating what structured data is already being parsed successfully.
The reasoning behind this step: The gap between "what AI can see today" and "what AI agents will need tomorrow" defines your optimization roadmap. Pages that AI engines currently cite have proven extractability. Pages that should be cited but aren't have structural problems to fix.
See your mentions across ChatGPT, Claude and Perplexity in real time, the moment buyers ask.
Linear or Jira creates development tickets from the roadmap, tagged with priority and estimated effort. Each ticket includes the specific schema type, target page, and expected citation impact from the Qwairy analysis.
Notion tracks implementation progress per page, with structured data status, before/after citation counts, and verification results. Useful for agencies running agent readiness audits across multiple client sites.
Google Search Console monitors structured data validity after implementation. Cross-reference with Qwairy's page presence to see whether valid schema correlates with improved citation status.
Slack posts alerts when a page moves from NOT_CITED to WORKING after a schema implementation, confirming the fix worked. Also alerts when Qwairy detects new AI crawlers attempting to access your site.
Google Sheets maintains a per-page agent readiness tracker with columns for schema status, rendering method (SSR/CSR), llms.txt inclusion, and citation status. For agencies, duplicate the sheet per client for portfolio management.
See Complementary Tools for the full list of MCP integrations.
The ROI of agent readiness is both immediate and compounding.
Immediate ROI from citation-based GEO. Every agent readiness optimization also improves chatbot citation. Adding structured data to a pricing page helps ChatGPT cite accurate pricing today AND helps AI agents extract pricing tomorrow. Sites with complete schema markup earn significantly more citations than sites without it. A 2-hour schema implementation on your top 5 pages can increase citation accuracy and frequency within weeks on Perplexity and within 4-8 weeks on ChatGPT.
Compounding ROI from agent traffic. AI agent traffic is growing exponentially. Gartner projects that by 2028, 33% of enterprise software interactions will be handled by AI agents. E-commerce brands report that agent-referred conversions run 2-3x higher than traditional web traffic because the agent has already pre-qualified the match before directing the user. Being agent-ready when this traffic arrives means capturing it. Being unprepared means the agent selects a competitor whose site it can navigate.
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What to look for: Check for three types of extractable data. Pricing information (can AI see your plans and costs?). Feature data (can AI read your feature comparison tables?). Product information (can AI extract product names, descriptions, ratings?). Any missing category is a priority fix. The keyword triggers data is especially revealing here. If AI engines trigger your brand for "affordable" but not "enterprise" and your product serves both segments, the structured data on your pricing page likely only exposes the lower tier. If triggers include feature names you highlight on your site, those features are extractable. Missing features indicate sections AI agents cannot parse, usually because the data is behind JavaScript interactions, accordion toggles, or embedded in marketing copy rather than structured tables.
What to ask Claude:
Based on the technical audit and extraction analysis, build me an agentic search readiness roadmap. Include: (1) structured data additions for key pages (pricing, features, products), (2) content restructuring for machine readability, (3) llms.txt optimization, and (4) a timeline with quick wins and long-term structural changes.
Tools Claude uses: Claude's reasoning layer synthesizes all previous data.
What Claude should produce:
Structured data priorities. Product schema, pricing schema, FAQ schema, HowTo schema for key pages. These help AI agents extract specific data points (price, features, steps) without parsing marketing copy.
Content restructuring. Convert marketing-style feature pages into structured comparison tables. Add explicit headings for each feature. Include machine-readable specifications alongside human-readable descriptions.
llms.txt optimization. Guide AI agents to your most important pages. Include structured summaries of what each page contains.
API-first content. For SaaS products, ensure API documentation is comprehensive and agent-navigable. AI agents increasingly complete technical evaluations by browsing API docs.
Timeline. Quick wins (schema markup, llms.txt) in Week 1-2. Content restructuring in Month 1-2. Architectural changes (SSR, structured data architecture) in Quarter 2-3.
📖 Related: What technical changes should I prioritize?
I want to prepare my site for AI agents that browse, compare, and buy. Walk me through this step by step:
1. Run a technical status check and pull page presence. Can AI agents access and navigate my key pages (pricing, features, documentation)? Also pull brand performance to check my overall source citation rate.
2. Pull source URLs for my domain, shopping insights, source domains with self-filter, and keyword triggers. What data can AI currently extract? What features and attributes does AI associate with my brand? What's hidden or unstructured?
3. Build an agent readiness roadmap: structured data additions, content restructuring for machine readability, llms.txt optimization, and timeline with quick wins and long-term structural changes.
Explain what each step reveals before moving to the next.
The effort-to-impact ratio. Adding llms.txt takes under an hour and gives every AI engine a structured roadmap to your site. Schema markup on key pages takes 2-4 hours per page. Server-side rendering migration is the only expensive item, and only applies to JavaScript-heavy SPAs. For most sites, the total agent readiness investment is under 20 hours of developer time spread across a quarter. For SaaS companies, e-commerce brands, and agencies managing client sites, agent readiness is the highest-leverage technical investment in 2026. The brands that are navigable by AI agents will capture a growing share of the purchase journey. The brands that aren't will be invisible to an entirely new discovery channel. Compare that to Qwairy plans starting at 59€/month.
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New playbooks documenting Claude + Qwairy MCP workflows for GEO operations are published regularly. Questions or feedback? Reach out on LinkedIn or at team@qwairy.co.