How to prepare your shop for AI shopping

At a time when online retail is under considerable pressure, competition is growing, and customer expectations are constantly rising, the use of AI-supported solutions is essential. They optimize business processes and increase the efficiency of customer service, marketing, logistics, and finance departments. Thanks to artificial intelligence in retail and the B2B sector, recurring work steps are automated and decisions can be made in real time.

This innovative trend involves not only chatting, but also active shopping. With new features from OpenAI and Google, companies have the potential to digitize their business in a completely different way. Well-known brands are personalizing experiences, optimizing product ranges, and predicting customer demand. Visitors don't need to waste their own time. They can delegate the task of searching for and comparing products and processing payments to the intelligent assistant. This makes shopping convenient and effortless.

Not sure whether your shop needs an AI agent or a chatbot? At Kenner Soft, we provide professional advice and develop a clear strategy.

What does the future hold for E-commerce?

The use of advanced technologies has significantly influenced digital shopping processes and brought about many changes in the digitization of retail.

Product discovery. Customers find items or services with the help of AI-powered search and recommendation engines. This allows them to find relevant options based on data analysis and consumer behavior. B2B buyers also prefer to shop via self-service.

Personalized experience. Generative AI offers personalized shopping experiences and individualized journeys for shoppers. Machine learning helps with pricing and promotions by creating unique experiences for consumers.

Order management. This is a crucial point, as customer expectations are rising dramatically. To meet them, AI-supported order management systems are necessary. They optimize many processes, from inventory management to delivery. This minimizes errors and improves the customer experience. Many processes are accelerated, increasing customer satisfaction and loyalty.

Payments. Various agentic commerce services such as Agent-Ready Payment from PayPal enable secure payments and transactions. Customers are no longer redirected to the shop. Payment is made exactly where a decision is made. This makes it much more likely that a purchase will be completed. Innovative real-time payments, embedded payment systems, and financial solutions improve security and user-friendliness for buyers.

Security. Customer trust must always be a priority. That's why shop operators invest heavily in various security features and measures. AI can be used for fraud detection and blockchain-based transaction verification. The Agentic Commerce Protocol (ACP) guarantees secure, fully automated purchases through AI assistants, preventing data loss and fraud. Human AgenticTrust verifies that interactions between shopping agents and humans are secure and trustworthy.

AI in E-commerce: Current developments and trends

According to the EHI study, most retailers consider artificial intelligence to be one of the most important future trends in e-commerce. Social, mobile, and conversational commerce, as well as hyper-personalization, are also very popular because they are revolutionizing search and product recommendations. Innovative technologies with classic and generative AI are being used to minimize abandoned shopping carts and increase sales.

AI-agents

The intelligent shopping assistants work independently, perform various tasks, and analyze customer data. They can then assist shoppers either in chats or via messaging apps by searching for items, placing orders, and making payments. Companies that actively use smart e-commerce solutions increase their business efficiency and customer satisfaction.

Visual search and augmented reality

Selecting products using images instead of text simplifies the search process, making e-commerce more efficient and easier. Visible items can be found more quickly. This is advantageous when visitors are unable to formulate an exact search query. Foreign customers can also order products in the shop if they do not have a good command of the language. Visual search is particularly recommended for B2B companies, as not all buyers can specify the technical details correctly. Image search allows the desired items to be found immediately.

Voice Shopping

Voice-controlled purchases have become a revolutionary trend. Searching by voice command is becoming increasingly popular. The reason is clear—it's easy. Voice commerce is preferred for repeat purchases of products when the customer is already familiar with the item and has no doubts.

What does Agentic Commerce mean for online shops?

It is a completely different approach to e-commerce when purchases are made not by humans but by AI-powered systems. These shopping assistants act on behalf of a buyer. They understand human language, analyze purchase history, and can interpret images and technical information.

The AI agent understands entire queries, orchestrates different sources of information, and executes transactions. You can tell it: Find a computer with an AMD Ryzen 5/7 processor, RAM (min. 16 GB DDR4/DDR5), and fast SSD storage (NVMe). This is beneficial for both the buyer and the retailer. The former doesn't waste any time, while the latter has more completed purchases. That's why the shop should not only be clearly visible, but also offer products with good data quality so that AI agents can recognize them.

Preparing data and systems for AI-agents

The data should be AI-ready so that purchasing assistants can not only see it, but also understand it. You should focus on its quality, strengthening governance, and open and scalable architectures. Follow these steps:

1. Data should be up-to-date, consistent, and complete. It is important to remove duplicates and use uniform formats. AI agents read structured formats better than pure HTML text. The quality of collaboration between AI systems depends on correct, consistent data. The Agent2Agent Protocol (A2A) only ensures data transfer, but does not guarantee its correct processing. If the information is incorrect or incomplete, the process is interrupted.
2. Metadata must be used and a schema defined.
3. Integration with these systems is necessary so that agents can access internal sources (CRM, ERP) and provide specific information.
4. Data from different sources must be collected centrally in order to analyze it. Unstructured data (images, emails, PDFs) is consolidated into flexible data lakes and structured data into data warehouses.
5. Unique identifiers such as SKU, GTIN, MPN, and manufacturer name are necessary for unique product assignment. For AI, semantically clear descriptions are much more important than ordinary marketing texts. Precise terms and consistent attribute names should be used.
6. Model Context Protocol is necessary for integrating AI agents with external data. This interface allows an online store to offer tools and resources to the AI assistant.

To integrate MCP, the following steps are necessary:

• Goal definition;
• Provision of APIs;
• Schema definition;
• Implementation of data validation;
• Setup of an MCP-compatible server endpoint;
• Authentication and authorization;
• Provision of context data;
• Integration of agents.

Technical aspects: Backend and performance optimization

In addition to protocol integration, it is also necessary to prepare backend performance correctly. Since the bytes sent by the MCP server consume tokens in the context window of LLMs, the number of JSON responses should be reduced. Descriptions for tools and parameters should be precise.

Creation of an MCP-server

To use MCP, you must create an MCP server. Only then can it be used in Claude for Desktop and other clients. Tools must be clearly defined and registered centrally. Next, a server connection to the MCP host is required. The next step is to provide three main types of functions. These include:

• Resources (data that clients can read);
• Tools (functions that LLMs call);
• Prompts (templates that visitors use to complete specific tasks).

To create a server, you need to be familiar with Python and LLMs (Claude). The next step involves logging in MCP servers. You can use STDIO- or HTTP-based servers. To do this, you need to use a logging library. The technical requirements are installation of Python 3.10 or higher and use of Python MCP SDK 1.2.0 or higher.

To query and format the data, you need to add helper functions. Next, you should implement tooling. Finally, the server installation and execution take place. Tests must then be carried out and any errors corrected.

Creation of an MCP client

An LLM-based chatbot client is used to establish the connection to MCP servers. This requires a Mac or Windows computer, installation of the latest version of Python, and the latest version of uv. Next, you need to set up API keys. This creates a customer. A method must be implemented to establish the connection to the MCP server. Core functionality is added to process queries and tool calls. Next, a chat loop and cleanup functionality are added. Finally, the main execution logic must be added. The most important points are:

• Initialization of a client;
• Server connection;
• Query processing;
• Resource management.

General adjustments

To ensure that everything works perfectly, the following points must be adjusted:

• Tool handling. Make the necessary modifications for the server to handle tool calls efficiently and take care of custom error handling for tool calls. Don't forget to implement tool-specific response formatting.
• Response processing. The tool results must be formatted. It is worth adding filtering or transformation of responses and implementing user-defined logging.
• User interface. For easier operation, you need to add a GUI or web interface, as well as command history or auto-completion, and implement rich console output.

Data strategies: Structuring and improving product data

The quality and structure of product data are important for an AI agent to function efficiently, because it uses the information provided to make comparisons and decisions. That's why it needs to be able to read and understand the information. Here are the best strategies for optimizing product data:

• Format standardization. If the attributes are consistent, AI assistants can compare and evaluate them.
• Machine readability. Markdown should be used instead of HTML to structure product data in the backend. LLM processes lists and tables much more efficiently than JSON.
• Data refinement. Ontologies and taxonomies should be used for better contextual understanding.
• Real-time updates. Data should always be up to date so that AI systems can immediately see updated prices and check availability.
• Unique IDs. Global standards (GTIN/EAN) are necessary so that AI-driven systems can compare prices on different platforms.

If you successfully implement these strategies, the product data will become an action-oriented knowledge base. Then the AI agents will trust them more and process them efficiently.

Step-by-step plan: Preparing your shop

If you want to increase your sales and sell your own products across platforms, you need to embrace new changes and innovations. The use of AI-based solutions is indispensable in this regard. To ensure that they can find and process your shop's offerings, do the following:

• First, it is necessary to structure the product information and make it machine-readable. The attribute structure should be clean. Descriptions, prices, and specifications must be provided correctly and in a standardized format, variants must be grouped logically, technical data must be completed, and consistent naming must be maintained throughout. Prices and stock levels should be synchronized in real time.
• Since AI shopping is done via API, you need to make your e-commerce platform API-enabled. AI assistants can access data and trigger actions via API.
• The next step is feed preparation.
• Clear rules for AI assistants should also be implemented. For monitoring purposes, all requests must be logged and analyzed using DataDome or other control systems.
• It is essential to ensure transparency and data protection, and to carry out checks with data protection audits and automated compliance scans.

Checklist

• Data structuring
• Completing product attributes
• API for ordering and inventory
• Preparing feeds
• Trust signals
• AI testing
• Monitoring If you need help with this, please contact our E-commerce Web Agency. Our professional team will advise you and help you with a variety of questions.

Conclusion

Retailers who want to prepare their shops for Agentic Commerce need to take care of certain technical and organizational requirements at an early stage. This is a crucial step that will have a major impact on the future of shopping.

In order for AI-supported systems to provide suitable offers and process information, online shops should offer clean, structured product data and open interfaces. The integration of the Context Protocol and Agentic Commerce Protocol modules, as well as compliance with data protection regulations, enables AI agents to access product information and external systems, and to standardize and automate retail processes. The protocols make the store competitive and guarantee security and efficiency. Equally important are automated price updates and real-time inventory management. Personalization and user-friendliness should be the focus. Preparation is therefore a useful investment in the future viability of the business.

If you would like to find out more or are looking for a reliable partner with secure, creative solutions, our Web Development Agency is the right place for you.

FAQ

• What does agentic commerce mean?

This term refers to a new, advanced form of online commerce in which AI agents act on behalf of humans to independently make purchases, compare products and prices, carry out transactions, and make decisions.

• What is the difference between agentic commerce and traditional e-commerce?

The main difference lies in two points: who decides and who acts. In e-commerce, humans make the purchases. In the other case, the shopping agent handles the purchasing process independently.

• Can AI agents compare prices and suggest alternatives?

Yes, modern AI systems operate autonomously, searching different websites, performing analyses, and comparing prices in real time. They analyze features and reviews and offer alternatives.