Contents
What?
Klarna unveiled the Agentic Product Protocol (APP) , an open standard and API designed to help AI agents automatically search, understand, and compare product offers online.
Why?
If shopping increasingly begins to be handled by intelligent assistants, the winners will be those sellers who provide accurate, up-to-date, and structured product data —because that's what the agents "work" with.
Who's it for?
online store owners , online sales managers, product feed, SEO, analytics, and automation specialists, as well as companies developing tools for the digital world.
Background:
Online commerce has relied on search engines, comparison sites, and marketplaces for years. Now, a growing trend is emerging: agentic commerce , where an AI agent not only helps find a product but also navigates the selection, recommendation, and purchase process. To achieve this, the agent must receive data that is complete (parameters, variants, availability), reliable (consistent identifiers), and fresh (current prices and stock levels). The app is intended to be a "common language" for such data.
What is Agentic Product Protocol and how does it work in practice?
APP can be thought of as a set of rules that describe how to publish product information in a way that's friendly to AI agents. Klarna promotes an approach based on structured objects (e.g., product and offer ), where the product describes "what it is" and the offer describes "under what conditions" it is sold (e.g., price, availability, variant, delivery). This allows the AI agent to perform less guesswork and more concrete data to compare.
“Products” and “Offers” – a simple change, a big effect
In many feeds, sellers confuse product descriptions with terms of sale, making automated inference difficult. In the APP model, separating these layers provides clarity: an AI agent can compare the same product (e.g., a specific model) across multiple offers and select the best one for the user. For an online store , this means refining data on variants, attributes, and identifiers.
"Live" instead of downloading and scraping
Klarna advertises the app as a solution designed to provide AI agents with up-to-date pricing and availability information , rather than static lists gathered from various sites. This is important because agents making purchasing decisions need to know whether a product is available "right now" and whether the terms of sale still apply.
Why is Klarna focusing on AI-driven trading?
Klarna is primarily associated with deferred payments, but in practice, it is increasingly developing an AI-powered search and purchase layer. In this approach, the APP serves as infrastructure: it aims to make it easier for AI agents to discover products and offers without having to manually "match" thousands of data formats. From a market perspective, it's an attempt to build a standard before only the largest platforms do.
If you are interested in technologies and implementations in the digital world, you can also visit the knowledge base: technologies and solutions .
What does this mean for online retailers?
More traffic and sales from the new channel
As AI agents become a popular way to search for products, a new "distribution channel" for purchasing decisions will emerge. Sellers who provide clear and complete data may receive more frequent recommendations. In practice, this could resemble today's comparison sites, but with a stronger emphasis on user context (e.g., preferences, budget, delivery time).
Pressure on product data quality
The APP rewards sellers who maintain a tidy catalog: accurate names, unambiguous attributes, complete parameters, good photos, and consistent identifiers. If the data is incomplete, the agent may "skip" an offer because they won't be able to confidently compare it with others.
Price competition can accelerate
The AI agent compares offers quickly and effortlessly. If the user requests "best option in 48 hours," the agent will focus on price, delivery, and availability. This signals to the seller that, beyond price, they need to strengthen elements that can be described in the data: warranties, return policies, shipping speed, reliability, and variant availability.
Risks: dependence on standards and control of the offer presentation
With the rise of agent-based shopping, the question arises of who controls the shopping "interface." If an agent acts as a proxy, the seller has less influence over the brand narrative than on their own website. Furthermore, there are issues of data quality (availability errors), price consistency, and potential dependence on implementation ecosystems.
How to prepare your online store for the era of AI agents?
- Organize your product catalog – standardize names, variants, attributes, units of measurement, colors, sizes and technical parameters.
- Take care of your identifiers – where possible, maintain consistent EAN/GTIN, SKU and clear variant designations.
- Separate the “product” from the “offer” – the product description is one thing, but price, availability, delivery and returns are another (and should be updated more frequently).
- Improve data freshness – inventory and prices must be refreshed quickly because the AI agent makes decisions in real time.
- Describe the terms of sale – delivery, returns, warranties, availability in parcel lockers, delivery time; these are elements that the agent can “understand” and take into account.
- Test feeds and APIs – monitor for errors, missing fields, and inconsistencies; implement integration-side validations.
- Think “SEO for agents” – precise attributes, clear parameters and complete information often work better than creative naming.
- Establish pricing policies – if the agent compares prices automatically, prepare a strategy for promotions, margins, and availability of bestsellers.
Will Agentic Product Protocol accelerate the transformation of online shopping?
APP signals that the digital world is entering a phase where "whoever has the better data wins the attention of the AI agent." For sellers, it's both an opportunity for additional sales and a challenge related to catalog organization and the speed of listing updates. If agent standards become widespread, companies that are already investing in data quality, automation, and information consistency throughout the sales process will gain an advantage.
Want to learn more?
Marcin Stadnik
e-commerce advisor
The author is a manager with extensive experience in e-commerce, sales strategy, and content marketing. He is a digital practitioner and consultant with over 15 years of experience in e-commerce projects, sales strategy, and online business development, as well as 25 years of experience in broadly defined distribution (offline and online). He specializes in creating and implementing effective solutions for online stores, supporting companies in developing their digital presence. He co-creates appropriate strategies for e-businesses, conducts audits, and oversees marketing activities—always combining analytical knowledge with market practice. He is the author and co-author of content published on the swiatcyfrowy.pl website—based on his many years of consulting, analytical, and operational experience. The materials created are intended to provide reliable, valuable knowledge that truly supports the development of online businesses. The content here is designed to address the real challenges and needs of companies operating in the e-commerce environment (the digital world).
