How to optimize for AI Agents? (That Browse, Compare, and Buy) - With Qwairy’s MCP

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.

Nicolas Ilhe•April 15, 2026•11 min read•

Why Agentic Search Changes Everything

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.

What This Playbook Produces

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.

Prerequisites

• 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.

1. Assess Technical Agent Accessibility

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.

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.

2. Analyze What AI Agents Extract Today

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.

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.

What to look for: Check for three types of extractable data: pricing information, feature data, and product information. Any missing category is a priority fix.

3. Build the Agent Readiness Roadmap

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.

What Claude should produce:

• Structured data priorities. Product schema, pricing schema, FAQ schema, HowTo schema for key pages.
• Content restructuring. Convert marketing-style feature pages into structured comparison tables.
• llms.txt optimization. Guide AI agents to your most important pages.
• API-first content. Ensure API documentation is comprehensive and agent-navigable.
• Timeline. Quick wins and architectural changes organized over a few months.

The ROI of Agent Readiness

The ROI of agent readiness is both immediate and compounding. Immediate ROI improves chatbot citation, while compounding ROI captures agent traffic as it grows exponentially. The effort-to-impact ratio demonstrates adding llms.txt takes under an hour, while schema markup may take 2-4 hours per key page, representing a manageable investment for substantial future benefit.