Anyone running an online store knows the routine: configuring discount campaigns manually, maintaining tiered prices for B2B customers, filling in missing product descriptions. Tasks that cost time but rarely create strategic value. With the update around Shopware Community Day 2026 in Cologne (June 10), Shopware has extended its Copilot with agentic capabilities. The AI assistant, part of Shopware Intelligence+ and currently in beta, understands requests in natural language, analyzes store data, and executes multi-step workflows. Changes only go live after the operator approves them. For manufacturers and wholesalers, the question is: which of these features actually save time in day-to-day operations, and where are the limits?
Agentic AI in E-Commerce: What Changes in 2026
Until now, AI assistants in shop systems mainly delivered suggestions or generated copy at the push of a button. Shopware Copilot goes a step further: it does not just handle individual tasks, it plans and executes connected workflows. Instead of merely drafting a product description, it can, for example, identify missing texts, generate them, translate them into several languages, and submit them for approval. This ability to coordinate several steps independently is what defines agentic AI.
Copilot works directly in the Shopware 6 admin area and draws on existing store data: sales figures, stock levels, customer groups, categories. Operators phrase their request in natural language, for example "Create a VIP discount campaign with 20 percent off and free shipping". Copilot analyzes which settings are required, configures the campaign, and shows a preview. Only after the operator reviews and confirms does the change go live. This approval logic is designed to prevent faulty or unintended adjustments from reaching the production store.
For midsize manufacturers and wholesalers this means: routine tasks that previously required multiple clicks and forms can be completed with a single instruction. Especially in B2B, where tiered prices, customer-group discounts, and seasonal campaigns are common, this promises real time savings. At the same time, control stays with the operator, who can review every change before publication.
Five Use Cases Shopware Officially Names
Shopware has published five concrete use cases that Copilot handles in the beta version:
1. Creating Discount Campaigns
Copilot configures promotions with discounts, free shipping, or gifts. Example: "Create a VIP campaign for existing customers with 20 percent off and free shipping above 100 euros order value." The assistant sets the conditions, selects the customer group, and creates the promotion. For wholesalers running regular seasonal campaigns, the manual configuration across several menus disappears.
2. Advanced Pricing Options and Tiered Prices
Particularly relevant for B2B: Copilot sets up volume discounts, such as "5 percent off from 20 items, 10 percent from 50 items". Tiered pricing is standard in wholesale, but maintaining it is laborious. Copilot takes over the configuration based on a simple instruction.
3. Lowering Prices of Slow-Selling Products
The assistant identifies items with weak sales, suggests price reductions, and moves the products into a sale category. That saves manual analysis and speeds up clearing slow movers.
4. Filling In and Translating Missing Product Descriptions
Copilot detects items with missing or incomplete descriptions, generates copy based on existing data (title, attributes, category), and translates it into additional languages. For manufacturers with large catalogs and international markets, this is substantial relief – much like other backend tasks that can already be automated with AI today.
5. Launching Campaigns and Multi-Step Workflows
Copilot can trigger more complex sequences, such as a campaign with product selection, discount, newsletter dispatch, and landing page. This feature is still in development, but it shows the direction: AI is not just taking over single tasks, it orchestrates entire processes.
What This Means for Manufacturers and Wholesalers
The agentic features of Shopware Copilot aim at efficiency gains through automating recurring tasks. For operators with large catalogs, frequent price adjustments, or multilingual stores, the benefit is clear: less manual work, faster execution, lower error risk in routine activities.
At the same time, limits remain. Copilot is in beta, which means: not all features are mature, and results must be reviewed. The approval logic is not an automatism but a safety gate. Anyone who blindly trusts suggestions risks incorrect prices, unsuitable copy, or misconfigured campaigns. Responsibility stays with the operator.
Another aspect: Copilot only works with the data available in the store. If structured attributes, categories, or sales figures are missing, the quality of its suggestions drops. Agentic AI in e-commerce requires clean data foundations. For many midsize companies this means: do the homework first, then use the AI.
With Shopware Nexus, announced for the near future, Copilot is also expected to connect external systems such as ERP and CRM via natural language. That would extend automation across the entire system landscape: syncing orders from the ERP, retrieving customer data from the CRM, controlling workflows across system boundaries. For wholesalers with grown IT landscapes, that could make the decisive difference.
Conclusion: Time Savings With Good Judgment
Shopware Copilot shows where e-commerce is heading: AI takes over not just individual clicks but entire task chains. Discounts, tiered prices, product copy, clearance – activities that used to cost time can now be handled with one instruction. For manufacturers and wholesalers looking to advance their store automation, this is tangible progress.
But agentic AI does not replace strategy. The approval step remains mandatory, the beta status demands attention, and the quality of the results depends on data quality. Those who treat Copilot as a tool that accelerates routine work will benefit. Those who expect the AI to make optimal decisions on its own will be disappointed.
If you want to assess which store tasks in your company can be accelerated by agentic AI and how to prepare your data foundations for it, a structured look at your processes is worthwhile. Efficiency does not come from technology alone, but from using it deliberately.
