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Build a shopping agent as a sequence of separate jobs: clarify what the shopper wants, match products precisely, retrieve offers and price history from sources that cover the relevant stores, assess whether a price is attractive, and present a recommendation. Keep checkout separate. A sale badge is not proof of a good deal, and no single documented API here provides complete price coverage across retailers.
What should the agent do, and in what order?
Use a pipeline in which each stage produces information the next stage can inspect. This makes it easier to explain a recommendation, spot bad matches, and prevent uncertain price data from silently becoming a purchase.
- Clarify the request. Capture the shopper’s intended use, required features, acceptable substitutes, budget, region and currency, condition, delivery needs, and whether they want a one-time comparison or ongoing alerts.
- Find and normalize products. Identify candidate products and variants using stable identifiers where available. Keep source-specific IDs as well as a normalized record.
- Retrieve offers and history. Query sources whose coverage fits the product and geography. Record the source and observation time for every returned value.
- Assess deal context. Compare an observed offer with a named price series over a stated time window, while accounting for shipping and other price components that the source exposes.
- Rank qualifying options. Compare only products that meet the shopper’s must-haves, then explain the price, history, and feature trade-offs.
- Ask before transacting. If checkout is supported, make it a separate step and get confirmation of the exact item and total before submitting an order.
Keep the shopper’s stated constraints separate from preferences the agent infers. That distinction lets the result explain whether an option won because it met a requirement or because the agent guessed it might be desirable.
How should it interpret a shopping request?
Turn natural language into explicit constraints
Represent the request in fields rather than relying on a single free-form prompt. Useful fields include product category, intended use, required attributes, preferred attributes, maximum budget, region, currency, new or used condition, delivery limits, and whether substitutes are acceptable. Mark each field as user-stated, inferred, or unknown.
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Ask a follow-up when ambiguity could change the result. For example, if a requested device is sold in materially different sizes or configurations and the shopper has not specified which they need, resolve that before ranking offers. Do not quietly turn an inferred preference into a mandatory requirement.
Keep must-haves distinct from preferences
A hard limit such as “must be the 1 TB model” should filter out other configurations even if they cost less. A preference such as “prefer a lighter model” can influence ranking without disqualifying every alternative. Preserve that distinction in the recommendation so a lower price cannot mask a failed requirement.
How can the agent avoid comparing the wrong products?
Product titles are not reliable identities: similar names can describe different generations, sizes, bundles, or conditions. Resolve candidates to a retailer product ID, ASIN, GTIN, SKU, or other stable identifier when the source provides one, and retain the original source identifier alongside the agent’s normalized record.
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Before combining offers, check the attributes that can change what the shopper receives:
- Manufacturer model and generation
- Size, capacity, color, or other configuration
- Bundle contents and included accessories
- New, used, refurbished, or open-box condition
- Seller and marketplace
Reject a candidate that conflicts with a must-have. If identity remains uncertain, show it as an unverified alternative rather than blending its price history with the requested product’s history.
Which sources should supply offers and price history?
Choose data sources by coverage, not by the promise of an all-in-one shopping API. A source can be useful for one marketplace or a merchant’s own catalog without covering arbitrary stores.
| Source | Documented role | What that means for this agent |
|---|---|---|
| Keepa API | Amazon marketplace product data and price histories, product search, deal browsing, tracking, and seller or offer information | Useful for Amazon-specific history and deal discovery. Keepa’s deal documentation says results include products updated within the prior 12 hours; that describes an update window, not complete real-time inventory. |
| Google Merchant API | Managing a merchant’s own Merchant Center catalog; the Products sub-API manages product inputs and processed products, including attributes such as price and availability | Useful when the builder is a merchant or has authorized catalog access. Its documented role does not establish it as a public feed for comparing arbitrary retailers. |
| Retailer or authorized partner interfaces | Coverage and fields depend on the specific retailer or partner | Use where available and permitted; verify current access terms, geography, and data rights before relying on the feed. |
Keepa’s deal endpoint supports up to 150 deals per request, and a query can page through up to 10,000 ASINs, according to its API documentation. Those limits describe that endpoint’s result handling; they do not guarantee that a desired product or every current offer will be present.
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Browser-based retrieval should be a source-specific fallback, not an assumption that every retailer permits automated collection. Check the relevant access terms and whether the page reliably exposes price, variant, seller, and availability. The cited documentation for Keepa and Google Merchant API does not establish scraping permission or API rights for other retailers.
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Evaluate a source before depending on it
- Coverage: Which stores, marketplaces, products, and regions does it actually represent?
- History: Which price series and seller types are recorded, and how much history is available?
- Freshness: How recently was the offer updated, and can the agent re-fetch it?
- Identity: Does the feed expose identifiers and variant attributes that support reliable matching?
- Price completeness: Are shipping, taxes, coupons, membership prices, condition, and availability included or omitted?
- Access: What are the provider’s quotas, licensing, reuse rules, and geographic limits?
For production, check current provider terms, quotas, data rights, and marketplace availability. The cited product documentation does not establish plan prices, account-specific quotas, licensing for republication, or historical coverage outside Amazon.
What offer data should the agent preserve?
Do not reduce an offer to a number. Store enough context to verify what it means and how it was obtained.
| Record | Why it matters |
|---|---|
| Retailer or marketplace and source | Defines where the offer came from and the limits of the comparison. |
| Product identifier and configuration | Shows which exact product or variant the offer represents. |
| Currency and observed price components | Prevents values in different currencies or with different included costs from being treated as equivalent. |
| Seller, condition, and availability | Distinguishes offers that may not be equivalent for the shopper. |
| Observation time and source update time, if available | Lets the agent assess whether an offer may have gone stale. |
| Shipping, coupon, tax, or membership-price fields, when supplied | Makes clear what is and is not included in the apparent total. |
Keep missing fields explicitly missing. If shipping, a coupon, or availability is not exposed, do not imply that the displayed price is a delivered, currently purchasable total.
How should the agent decide whether a price is a deal?
Name the comparison before calculating it
For every historical comparison, define the source, price series, lookback period, and timestamp. “Below the tracked Amazon price series over the past 90 days” is interpretable; “a great deal” without a reference is not. Keep the currently observed offer separate from the historical reference.
A practical result can show the current observed price, the chosen historical reference (such as a range or median), the period covered, and how far the current observation sits from that reference. The agent should use only values returned by its source; it must not fill gaps with guessed prices.
Use careful language about historical lows
A historical minimum is meaningful only within the selected source’s captured series and time window. Do not call an offer “best ever” or an “all-time low” unless the source’s coverage supports that scope. A discount label by itself does not demonstrate that the item has been cheaper than usual or that the reference price is comparable.
Refresh before calling an offer current
Stored offers can become outdated. Keepa’s offer documentation says price and shipping are stored separately in the described fields and warns that offer histories can contain outdated offers. Re-fetch offer data before reporting a live deal, and identify any missing shipping or coupon information that could change the total.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA transparent report should read along these lines: “The offer is [observed price] before shipping, observed at [time]. The [named source and marketplace] price series was [historical reference] over [window]. Shipping or coupon data is not included in this comparison.” Insert only values the agent actually retrieved.
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How should it rank and explain alternatives?
First remove candidates that fail a must-have. Then rank the remaining options against the shopper’s priorities, using a consistent comparison:
- Exact product and configuration match
- Delivered total where the source documents shipping and applicable discounts
- Seller, condition, and availability
- Historical-price position and period covered
- Feature fit and meaningful trade-offs
- Data freshness and source coverage
Explain why the leading option ranks first and identify the compromise that matters most. For example, an option may be cheaper before shipping but have a less complete total, or it may have a stronger price-history position while coming from a narrower source. Do not let a low price compensate for an unmet required feature.
Where should checkout fit?
A useful first version can stop at recommendations and optional price alerts. If the agent can place orders, make that a distinct transaction stage rather than an automatic consequence of ranking. Before submitting, show the exact product and configuration, seller, condition, total, and delivery details, then require the shopper’s confirmation.
AWS describes agentic commerce as potentially extending from product discovery and price monitoring through purchasing, depending on user choices. It also discusses transaction APIs as an alternative to UI scraping or manually filling forms. This is architectural guidance, not a universal retailer policy or legal requirement; availability and rules depend on the transaction service and retailer.
What should a first implementation deliver?
Start with a read-only comparison for one supported source and a clearly bounded product category or marketplace. Make each recommendation traceable to its product match, offer observation, historical series, and timestamp. Add other retailer feeds only when their rights, identity quality, geography, price fields, and freshness are understood.
Amazon has described consumer-facing price tracking in different terms across products: Amazon’s 2025 Rufus announcement described 30- and 90-day tracking, while Amazon Ads reported on June 11, 2026, that Alexa for Shopping could show up to a full year of price history. These are vendor descriptions of their own features, not independent measurements or guarantees that a third-party agent can access equivalent data.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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