Agentic Commerce: AI Shopping Agents for Ecommerce

TL;DR: “Agentic commerce” is the shift from AI that suggests to AI that acts — researching, comparing, shortlisting and shaping what customers buy. The fastest wins aren’t flashy: they’re operational. If your product data is messy, your PDPs are vague, or your shipping/returns are unclear, you’ll lose visibility in the new decision layer.

What is agentic commerce

Agentic commerce is when an AI system can complete multi-step tasks for a shopper, such as:

• gathering requirements (“best running shoes for flat feet under $200”)
• comparing options against constraints (delivery time, returns, ratings)
• summarising trade-offs
• recommending a shortlist
• and increasingly, nudging the purchase decision (or triggering the purchase inside a platform)
  

This isn’t a future concept. It’s the direction search, shopping and recommendations are moving as AI becomes the interface for product discovery.

Why this matters: the funnel is getting shorter

Traditional ecommerce assumed customers would:

1. land on a category page
   
2. browse
3. open multiple PDPs
4. decide
5. checkout
   

Agent-driven discovery compresses this into:
question → shortlist → decision.

That means:

• fewer page views before purchase
• less tolerance for unclear product info
• a higher premium on trust signals
• more demand for comparability (clear attributes, clear policies)
  

If your store can’t be confidently interpreted and compared, it becomes “low certainty” — and low certainty gets filtered out.

How AI shopping agents decide: the 5 decision signals

Think of agents as having a simple scoring model:

1. Relevance: does it match the user’s needs?
2. Clarity: can the agent parse your product attributes reliably?
3. Risk: are shipping/returns/warranty clear and fair?
   Trust: reviews, legitimacy, contact details, consistency
4. Value: price + delivery + returns + proof (not price alone)
   

Your job is to raise certainty on all five.

The agent-ready ecommerce checklist (what to fix first)

1) Product data that is consistent everywhere

You need alignment between:

• your PDP content (what humans read)
• your product feed (Google/Merchant feeds, marketplaces)
• your structured data (schema)
• your variant logic (size/colour/pack count)
  

Common breakages:

• different prices between PDP and checkout
• “in stock” on PDP but unavailable at checkout
• variant titles that don’t specify the actual difference
• mismatched sizing (AU vs US) without clarification
• image sets that don’t match the selected variant
  

Fix in practice (quick rules):

• Standardise naming conventions: Brand + Model + Key Attribute + Variant
• Use one unit system consistently (and convert where needed)
• Make variant options explicit: colour names, sizes, pack counts
  

2) PDPs that are “citable” (AI-friendly formatting)

Most PDPs are conversion-optimised for humans but not for machine clarity. Your PDP should include these repeatable blocks:

Above the fold

• one-sentence “what it is / who it’s for”
• 5 bullet benefits (no fluff)
• variant clarity (colour/size/pack)
• price + stock + dispatch time
  

Mid page

• specs table (dimensions, material, compatibility, inclusions)
• “who it’s for / not for” (reduces returns and increases trust)
• care instructions / maintenance (if relevant)
  

Bottom

• shipping and returns summary (plain English)
• FAQs (real questions)
• reviews with distribution (not just a single testimonial)
  

PDP mini-template (copy/paste structure)

• Best for: [use case]
• Key benefits: (5 bullets)
• Specs: (table)
• What’s included:
• Sizing/compatibility:
• Shipping: dispatch + delivery windows
• Returns: timeframe + conditions
• FAQs: 6–10 questions
  

3) Shipping and returns that reduce risk (Australia-specific)

AU shoppers often care about:

• metro vs regional delivery times
• PO boxes / parcel lockers
• express availability
• returns cost responsibility
• clear “change of mind” policy
  

Make the policy short and specific:

• Dispatch: “Ships in 1–2 business days”
  
• Delivery: “Metro 2–5 days, regional 4–10 days”
  
• Returns: “30 days, unused, original packaging”
  
• Refund timing: “Processed within X business days”
  

Vagueness creates friction. Friction gets you excluded from shortlists.

4) Trust signals that make you look “low-risk”

Agents lean on proof. Add:

• recent reviews (with dates)
• verified purchase markers (if you have them)
• clear contact page and support hours (AEST/AEDT where relevant)
• business legitimacy signals (ABN and address if appropriate)
• warranty basics and how claims work
  

5) Comparison content that wins the shortlist

If the shopper asks “best” or “which should I buy”, comparison pages become the answer engine’s fuel.

Publish:

• Best [category] for [use case] in Australia
• [Product A] vs [Product B]: what’s the difference?
• How to choose [category] in 5 steps
  

Structure the comparison for reuse:

• quick verdict (1–2 lines)
• table of differences
• who should pick which
• FAQs
  

6) Avoid the biggest agent-killer: contradictions

These are the fastest ways to lose “trust”:

• different info in the feed vs PDP
• unclear bundles or pack counts
• missing specs in one place and present in another
• “from” pricing that doesn’t match variants
  

7-day implementation plan (actionable)

Day 1: Audit top 20 SKUs: titles, variants, images, stock, policy references
Day 2: Roll out PDP template to top 10 SKUs
Day 3: Fix shipping/returns pages + add dispatch time on PDP
Day 4: Feed hygiene sprint (titles, GTIN/MPN, images, variant mapping)
Day 5: Publish 1 “best for” guide and link from category pages
Day 6: Publish 1 “vs” page targeting a real competitor / alternative
Day 7: Add FAQs to PDPs + measure conversion lift and support-ticket change

What to measure (so you know it’s working)

• organic CTR for “best” / “vs” queries
• conversion rate by landing type (PDP vs guides)
• add-to-cart rate after PDP template update
• return rate changes (often improves with “who it’s for/not for”)

support tickets per 100 orders (FAQs reduce this)

FAQs