E-commerce is facing a fundamental change – and most online shops have not yet noticed it.
Jan Otto, Founder of JANO Consulting, describes in his guest article in the E-Commerce Magazine the transition to Agentic Search – an approach in which purchase intent, not keywords, drives search results. This approach becomes tangible in the shop as an Agentic Shopping Assistant and enables a form of AI Product Discovery, which replaces traditional filter categories and keyword searches. As a concrete real-world example, Jan Otto cites aginity.ai.
The End of the Keyword Era: Why Users No Longer Think Like Search Engines
Generative AI has fundamentally changed user behavior. Customers today type natural language queries with context, budget, and purpose – and expect the system to understand them.
Millennials Prefer Dialog-Based Search
67% of Millennials prefer a dialog-based, contextual search over classic keyword search.
Higher Purchase Probability
Users who search in natural language have a 35% higher purchase probability.
Mobile Search Queries > 5 Words
43% of all e-commerce search queries on mobile devices now contain more than 5 words.
Why Classic Product Search is Reaching its Limits
Traditional search solutions are designed for keyword matches – not for purchase intentions. This leads to three serious problems in practice.
Zero-Result Problem
Over 70% of users leave the shop immediately when no results are displayed – even though the product would be available in the catalog.
Relevance Problem with Complex Queries
The more context a query contains – purpose, budget, body size – the worse traditional search systems perform.
Returns Problem Due to Poor Result Quality
Wrong purchases due to unsuitable search results cause returns. Each return costs 17–25 euros – regardless of the product price.
Generative AI as the New Infrastructure for Product Search
Agentic Search analyzes purchase intent, extracts relevant product attributes, and directly matches them with the product catalog – structured, traceable, and justified. You can find a detailed technical comparison in the article What is Agentic Search?.
RAG vs. Agentic Search in comparison
| Approach | RAG (classic) | Agentic Search |
|---|---|---|
| Data basis | Vector index from product texts | Structured product data in the catalog |
| Query logic | Similarity search | Attribute-based intent matching |
| Hallucination risk | Increased (generative output) | Significantly minimized (structured product data) |
| Explainability | Low | High / Attribute-based traceable |
| Update effort | High (re-indexing) | Low (live catalog) |
Agentic Commerce: When every product becomes an expert
In classic systems, the product catalog is passive. In the agentic model, every product becomes active: It gets its own AI agent that independently responds to inquiries. Für Kunden wird dieser Ansatz als Agentic Shopping Assistant erlebbar.
Step 1 – Catalog analysis
An agent generator analyzes the entire product catalog. For each SKU, all relevant attributes are captured, weighted, and structured.
Step 2 – Intent extraction
An intent module analyzes the request and extracts the purchase intent: What is the user looking for? Which attributes are important?
Step 3 – Agent Activation
Based on the recognized intent, only the product agents whose products are potentially relevant are activated.
Step 4 – AI Product Discovery with Justification
Each search result comes with a justification at the attribute level – transparent and traceable.
aginity.ai: The concrete example from the E-Commerce magazine
Jan Otto explicitly names aginity.ai as an example of how Agentic Commerce is already functioning in practice today. aginity.ai works with structured product data and attribute-based direct catalog queries – traceable instead of purely probabilistic.
Minimized hallucination risk
Since recommendations are based on catalog-based product attributes, the risk of misinformation is significantly lower than in purely generative RAG approaches.
Direct catalog query at the attribute level
aginity.ai works with structured product data and attribute-based direct catalog queries – traceable instead of purely probabilistic.
GDPR-compliant in the EU
Hosting in the data center Frankfurt/Main – designed for AI Act requirements.
Gemessene Ergebnisse aus laufenden Projekten
Strategic positioning: Why the timing is crucial now
Companies that do not invest in intelligent product search now will fall behind – not in five years, but in two.
Expectations are rising quickly
Customers who are familiar with ChatGPT, Perplexity, or Google AI Overviews have a new benchmark for 'good search'.
Competitors are catching up
Large e-commerce players are massively investing in AI-supported search. For medium-sized retailers, Agentic Search is the most realistic way to keep up.
Integration window is now open
Systems like aginity.ai can be integrated into existing shop systems via an API. The effort today is 7 days.
Conclusion
Agentic Commerce is not science fiction – it is a proven technology that can go live in seven days: GDPR-compliant, with minimized hallucination risk through structured product data and measurable results from day one.
Frequently Asked Questions
What is Agentic Search?▾
Agentic Search analyzes the purchase intent behind a query, matches relevant product attributes in the catalog, and provides justified recommendations at the attribute level. This improves traceability and minimizes hallucination risks compared to purely generative RAG approaches.
What is AI Product Discovery?▾
AI Product Discovery describes the ability to recognize the actual use case of the buyer from a natural language query and find suitable products from the entire catalog – intent-based, at the attribute level, with explainable results.
What is the difference between Agentic Search and RAG?▾
RAG systems generate answers based on retrieved text passages from vector databases. Agentic Search works with structured product data and attribute-based matching – explainable and with significantly minimized hallucination risk.
What is the difference between an AI shopping assistant and a chatbot?▾
A chatbot engages in open conversations. An Agentic Shopping Assistant provides precise product results from the real catalog – attribute-based justification, no free conversation.
Do I need to change my shop system for Agentic Search?▾
No. aginity.ai integrates as an API layer into existing systems. Shopify, Shopware, and Magento are natively supported.
How long does the integration take?▾
For a typical product catalog with 1,000–50,000 SKUs, the integration time is 7 business days – including setup, testing, and go-live.
Are customer data stored or used for training?▾
No. Search queries are processed in a pseudonymized manner. No customer data leaves the EU data center in Frankfurt/Main.
What does the deployment cost?▾
aginity.ai offers three monthly packages: Agentic Chat (from 750 /Mo), Agentic Search (from 1,000 /Mo), and the Agentic Suite (from 1,500 /Mo) – each in the local currency plus VAT. All packages include 10,000 products and 10,000 queries/month.
Further content
About the Author
Jan Otto is the founder of JANO Consulting and advises medium-sized retail companies on digital transformation. His focus is on data-driven strategies for e-commerce, omnichannel, and AI integration. This text originally appeared as a guest contribution in the E-Commerce Magazine (02.02.2026).