Prepare your store for the age of AI shopping agents
Run a comprehensive audit to evaluate how discoverable and frictionless your catalog is for autonomous agents.
Agent-Readiness Dashboard
Audited via Agent-Ready Framework
Needs Work (Yellow)
How well this page can be interpreted, executed, and trusted by autonomous shopping agents.
- Company
- MANGO
- Sector
- Fashion & Apparel Retail
- Audited URL
- shop.mango.com
- Avg. monthly visits
- 10M+
Interpretability
Can an agent read and understand the product data?
Executability
Can an agent complete a purchase from the markup?
Reliability
Is the exposed product state consistent and trustworthy?
Findings
- errorCode#01
The canonical URL points to color 99 while the audited page represents color 37, creating an identity mismatch for the selected product variant.
Suggested fix
Use a canonical URL consistent with the selected color variant, or define a stable parent-product canonical URL while exposing the active color and variant identifiers in structured data.
- errorCode#02
Product schema uses partial Microdata but does not provide a complete machine-readable Product and Offer representation. The offers lack clear availability, seller, item URL, variant identity, and an unambiguous relationship between the current and crossed-out prices.
Suggested fix
Add comprehensive JSON-LD using Product, Offer, and, where appropriate, ProductGroup or individual variant entities with sku, color, size, price, priceCurrency, availability, seller, url, and valid-through information.
- warningCode#03
The page exposes rich product and inventory data inside framework-specific React payloads, but the purchasing operation is represented primarily by JavaScript buttons without a declarative machine-readable action or add-to-cart contract.
Suggested fix
Expose an agent-consumable add-to-cart endpoint or form contract documenting product ID, color ID, size ID, quantity, inventory validation, and expected success or error responses.
- warningUI/UX#04
Size buttons expose availability text but do not clearly expose the selected size state through semantic attributes such as aria-pressed or aria-selected, and the size collection is not grouped with a clearly associated accessible label.
Suggested fix
Use a fieldset and legend or an equivalent labelled group, and update aria-selected or aria-pressed on the chosen size while exposing the selected color and size in accessible state text.
About this Audit Dashboard
What it solves
Identifies critical conversion friction by evaluating e-commerce performance through both Human-Centric Heuristics (Jakob Nielsen) and AI-Readiness Frameworks (ICEME 2026). It ensures your store is optimized for both human shoppers and autonomous web agents.
How it works
Our agent-driven audits analyze your site across three dimensions: Interpretability (semantic clarity/machine readability), Executability (action pathways/API reliability), and Decision Reliability (evidence/temporal validity signals). The dashboard maps these findings into actionable recommendations.
Built with
A modern stack using React and Tailwind CSS, deployed on Vercel. Powered by real-time automated workflows via Make and GitHub API webhooks to deliver instantaneous performance scores.
