Direct answer: An agentic-commerce readiness audit checks whether a Shopify store's product truth, discoverability, buying path, permitted actions and data remain coherent when a person or AI agent tries to use them.
The five layers below are an inspection sequence, not a compulsory package. Prioritize findings by customer harm and commercial consequence. An approved repair belongs to Machine Experience; Moving Target watches what changes after it is implemented.
Why audit before implementation?
An audit should reveal what to preserve, what to repair and what must be designed before execution. It is a diagnostic step scoped to the merchant's objective.
Layer 1: Product and catalogue architecture
Review products, variants, collections, metafields, tags, source-of-truth rules and machine-readable product context.
Layer 2: SEO, AEO, GEO and Machine Experience
Review metadata, headings, internal links, FAQs, structured data, entity consistency and whether humans and agents can understand the store.
Layer 3: Commerce architecture
Review checkout, B2B requirements, markets, payments, apps, custom logic and any ERP/CRM/WMS dependencies relevant to the merchant.
Layer 4: Automation and operations
Review Shopify Flow, Sidekick use, apps, custom automations and manual processes. The goal is to choose the right execution layer rather than automate everything.
Layer 5: Data integrity and validation
Review inventory, customer/order data, analytics, event quality, metafields and integration reliability. Agentic systems are only as useful as the state they can trust.
What happens after the audit?
CAT converts findings into a controlled sequence: Define → Decompose → Orchestrate → Execute → Validate → Learn. If implementation is approved, one objective can be scoped as a Shopify Agentic Automation through Codified Algorithmic Thinking™ under current CAT pricing.
