AI agents are already recommending your products to shoppers. Some of those shoppers buy. Many don't. The gap between the recommendation and the purchase is almost always a listing problem: the agent didn't have enough confidence in the product data to push the recommendation over the line. This playbook shows you how to find those products and fix them.
Goal: Conversion rate improves on targeted products within two to three weeks of APL creation.
Time to complete: 30–45 minutes to identify and set up; ongoing as you apply APLs.
Features used: AgentIQ Overview → Analytics Dashboard · Prompt Libraries · My Store → Agentic Product Listing (APL)
Step 1: Find Products Where Agents Are Recommending but Not Converting
AgentIQ Overview — Agent Traffic Analytics showing sessions, conversion rate and attributed revenue by product
The Analytics Dashboard shows you which products agents are already recommending, and which aren't converting after the recommendation. High sessions with low conversion is the signal you're looking for.
Go to AgentIQ Overview.
Scroll to the Best Attributed Products section.
Sort by Sessions to find the products getting the most agentic traffic.
Cross-reference with Conversion Rate. Products with high sessions and low conversion are ones agents are recommending, but something in the listing is stopping shoppers from completing the purchase.
Note the top three to five products in this pattern. These are your targets for this playbook.
📌 Tip: Also check the AI Agents Distribution section. If most of your traffic is coming from one specific agent (ChatGPT, for example) and conversion is low specifically there, the issue may be a listing gap that matters more to that agent's ranking logic.
Step 2: Build a Purchase-Intent Prompt Library for Each Target Product
The prompt table, sorted by Opportunity from high to low, with Funnel Step, Alignment, Ranking and Volume per prompt
A product that converts well in agentic channels has an Agentic Product Listing optimized for high-intent prompts: the ones where a shopper has already decided to buy, not just browse. Your Prompt Library is where you find those prompts, and you no longer have to guess which ones they are.
Go to Prompt Libraries and create a new library for your first target product. In the description, brief the agent on the product, the buyer and the type of purchase-intent conversations you want to surface.
On the Add sources step, use Discover subreddits to find communities where your buyers discuss this product before buying, not just after. Buying advice and product review communities tend to generate higher-intent prompts than enthusiast communities. If you've built a library for a similar product before, Select existing sources reuses those sources without a re-crawl.
Leave Optimize with keyword volume checked. Without it the prompts come back unscored, and the whole point of this step is to rank them.
Once the library is Ready, open it and filter Funnel Step to Purchase. That does the job you used to do by reading prompt wording for buying signals like "best X for Y" or "which X should I buy".
Leave the table on its default Opportunity sort and take the top two or three. Pay attention to prompts with real Volume and a Ranking of 0 or a weak Alignment: that combination is exactly the gap this playbook exists to close, because it means shoppers are asking in numbers and your catalog isn't there to answer.
Step 3: Create Agentic Product Listings Optimized for High-Intent Prompts
The Agentic Product Listing is the agent-optimized version of your product, stored as Shopify metafields. It increases agent confidence at the final recommendation step: the moment when the agent decides whether to include your product in its response or skip it.
In My Store, find your first target product in the Product Optimization Opportunities table and click Create APL (or View APL → Edit prompts if one exists).
Select the two or three prompts you shortlisted in Step 2. Because they came off the top of the Opportunity sort with Funnel Step set to Purchase, the generated listing gets optimized for the moments when shoppers are most likely to convert rather than for research queries that never close.
Click Run agentic listing. AgentIQ pulls in competitor data and generates a more competitive Agentic Product Listing. The run takes a few minutes, so click Run in background if you don't want to wait.
Review the generated Unique Selling Point, Agentic Title, Agentic Description and Top Features, and edit inline if anything needs adjustment.
Repeat for each product on your shortlist.
One-time setup: AI agents only see your APLs after the AgentIQ fields are mapped into your Shopify Catalog. If you haven't done this yet, follow Mapping Agentic Product Listings to Your Shopify Catalog.
📌 Note: The APL structures data in a way that increases agent confidence at the final recommendation step. A product with a well-structured APL is more likely to be recommended with confidence, and a confident recommendation converts at a higher rate than a hedged one.
Step 4: Measure the Impact
Wait two to three weeks after applying the APLs.
Return to AgentIQ Overview and check the conversion rate on your target products.
Compare the Attributed Revenue before and after for the same products. If conversion rate improved, the APL is working.
Reopen the library and check the Ranking column on the prompts you targeted. Movement there confirms the listing changes landed, even before the revenue numbers catch up.
For any product that still isn't converting, go back to My Store, open the product and check the Prompts Analysis panel with your target prompt. The Analysis Results panel will show exactly which listing gaps are still breaking the handoff.
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