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Learn how Agentic AI differentiates itself from Generative AI, bringing new levels of autonomy and adaptability to AI. The article presents examples of applications in e-commerce, user-centric design and business automation, as well as practical steps for adopting Agentic AI in business. Find out what the challenges, ethical questions, and how you can leverage the capabilities of Agentic AI for competitive advantage and improved user experience.
Agentic AI is the new horizon in the evolution of artificial intelligence, bringing with it radical changes in the way businesses and professionals approach digital transformation and process optimization. In contrast to Generative AI that focuses on content creation, Agentic AI is characterized by a new dimension: autonomy and the ability to make decisions and perform actions independently. AI agents based on Agentic AI are becoming increasingly widespread in areas such as e-commerce, robotics, business automation and process personalization, offering significant opportunities for brands and professionals.
The main difference between Agentic AI and Generative AI is the degree of autonomy and adaptability. Generative AI focuses on creating content (text, images, videos), but depends on human prompts. Agentic AI, on the other hand, creates autonomous action plans, formulates strategies, and acts based on the user’s goals, often without the need for constant human guidance. For example, an Agentic AI system can completely take over the management of an e-shop, from replenishing inventory to optimizing advertising costs—without asking for commands each time.
In e-commerce platforms, Agentic AI can automatically renegotiate prices with vendors, identify inventory shortages and plan marketing campaigns based on continuous data analysis. In industry, Agentic AI detects malfunctions in production lines and takes action, offering automatic maintenance and reducing downtime. In digital assistants, the new generation of Agentic AI can manage schedules, reservations or even personal tasks, learning the user's preferences for a truly personalized experience.
The introduction of Agentic AI does not mean that the human experience takes a back seat. On the contrary, it imposes a new philosophy on user-centric design. Designers are called upon to build interfaces and services where the user has control over AI agents, defining boundaries, preferences and levels of autonomy. A structured example of user-centric design with Agentic AI in e-shops is the existence of a dashboard that allows the merchant to adjust the rules of behavior of the AI according to seasonality, stock levels or marketing priorities.
According to a recent McKinsey study (2023), companies that implemented agentic AI systems improved efficiency by up to 30% in supply chain processes, while customer satisfaction increased by 22%. A typical example is a multinational retail company that transferred a large part of its workflows to autonomous AI agents, freeing up valuable human resources for more strategic roles (source: McKinsey Global Institute).
Successfully integrating Agentic AI requires a clear strategy and understanding of its capabilities. The key steps that professionals and online stores should follow to transition to Agentic AI are:
Goal setting and usage assessment: Identifying the areas where AI autonomy can deliver the most (e.g. inventory management, customer support, personalization AI) is the first step.
Choosing appropriate platforms and tools: There are already many Agentic AI platforms, either for ready-made automation solutions or for custom solutions. The choice should be based on criteria such as compatibility with existing systems, scaling capabilities and level of security.
Rule setting, monitoring and ethical governance: Agentic AI requires clear operating rules and constant monitoring to ensure alignment with business policies. Ethical issues, such as ensuring transparency (ethics in AI), are of paramount importance.
The introduction of Agentic AI raises ethical and technological challenges that need to be addressed, especially in terms of data privacy and transparency in decision-making. Its development must be combined with strict data protection policies, clear information to users about how AI agents operate and their degree of autonomy. There is also the risk of bias or incorrect decision-making due to incomplete or biased datasets, which highlights the importance of controls in training Agentic AI systems.
Agentic AI marks the next step in the maturation of artificial intelligence. With its rapid adoption, especially by professionals and online stores, is shaping new operating models that combine AI autonomy, user-centric design, automation innovation, and the demand for ethics and transparency. By implementing Agentic AI with strategy, training, and a focus on the user experience, businesses can fully leverage its capabilities, improve system efficiency, and gain a significant competitive advantage for years to come.
Sources:
https://smashingmagazine.com/2026/01/beyond-generative-rise-agentic-ai-user-centric-design/
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
