Agentic AI: what it is and how it transforms ecommerce

The universe of artificial intelligence continues to advance. After adopting predictive AI to anticipate behaviours and generative AI to create content from scratch, we have crossed the starting line into a new technological frontier: the era of agentic AI (agentic AI).

In this new landscape, we are no longer talking about passive systems that wait for step-by-step human instructions. We are witnessing the emergence of agents capable of reasoning, making complex decisions, and executing actions completely autonomously in order to achieve a specific goal.

This evolution is transforming the landscape towards agentic enterprises. In fact, according to the consultancy Gartner, 33% of companies are expected to use agentic AI by 2028. Autonomous agents are no longer science fiction; they are the driving force redefining entire sectors, with ecommerce being one of the most affected by this revolution. But what exactly does this involve, and why is it changing ecommerce? We explain it in detail in this article.

What is Agentic Artificial Intelligence?

According to  Google, this technology is an “advanced form of artificial intelligence focused on autonomous decision-making and actions.” They add that it is a subset of generative AI that focuses on the orchestration and execution of AI agents that use large language models (LLMs) as a “brain” to perform actions through various tools and achieve higher-level objectives.

How is it applied in a real example? While generative AI can be used to create materials for a marketing automation strategy, agentic AI can take care of deploying them across the relevant channels, monitoring performance, and automatically adjusting the strategy based on the results.

To understand how agentic AI operates within companies, we need to analyse the three pillars that underpin it:

• Autonomy: Agents are able to carry out complex, multi-step tasks independently, removing the need for continuous human oversight.
• Adaptability: They are not rigid. They can learn from their daily interactions, receive feedback from their environment, and adjust or change their decisions based on context.
• Goal orientation: Instead of being given structured, step-by-step commands (as was the case with traditional software), an AI agent is assigned a high-level final objective (for example: “Increase the open rate of an email marketing campaign by 2%”). The system applies logic and reasoning to autonomously determine the most efficient path to achieve it.

Therefore, agentic AI represents a shift from systems that respond to systems that act in order to solve complete objectives intelligently and autonomously.

Advantages of Agentic AI

 The adoption of agentic AI provides competitive advantages that can transform the way businesses operate. These can be summarised in the following key points:

• Maximum adaptability and operational efficiency: It automates end-to-end workflows and makes independent decisions. This speeds up internal processes, reduces operating costs, and generates significant time savings.
• Hyper-personalised experiences: By mirroring human cognitive processes, it delivers highly personalised, real-time assistance. This ensures intuitive and seamless interactions, leading to greater user satisfaction and loyalty.
• Data-driven decision-making: Thanks to advanced agentic data management, the system is capable of processing large volumes of information in real time, detecting hidden patterns, and anticipating outcomes, enabling fast action based on strong empirical evidence.
• Increased productivity: It frees teams from repetitive and mechanical tasks. As a result, professionals can focus on strategy and creativity while AI executes processes with greater precision.

Differences between predictive AI, generative AI and agentic AI

Feature	Predictive AI	Generative AI	Agentic AI
Main function	It analyses historical and mathematical data to anticipate future patterns or behaviours.	It generates original and new content (texts, images, emails, code) based on existing data.	It plans, reasons, makes decisions, and executes complete workflows independently.
Mode of operation	It responds to specific analytical queries based on static variables.	It requires a direct human command or prompt to generate each piece of content.	It works proactively, pursuing a global objective through a continuous cycle.
Flexibility	Very rigid. If the environment changes or new untrained variables appear, the model fails.	Moderate. It adapts to the context of the prompt received, but is unable to interact with the external environment.	Fully dynamic. It evaluates the environment in real time, learns from feedback, and changes strategy if necessary.
Degree of autonomy	Low. It is a query-based tool; any actions derived from the analysis are carried out by a human.	Medium. It automates content creation, but fully depends on explicit user instructions.	Maximum. It can operate autonomously and call external software without human intervention.
Practical example in ecommerce	It analyses the database to predict which customers have a high probability of abandoning a cart.	It drafts a persuasive email template to encourage recovery of that cart.	It integrates with the CRM, detects cart abandonment, designs a personalised offer in real time, triggers the email, and adjusts the campaign.

How does agentic AI work?

To explain in a simple way how agentic AI works, Google notes that instead of merely following rigid instructions, these systems operate in a fluid manner through a continuous cycle based on five key steps carried out by AI agents:

• Perception: The system begins by extracting data from its environment and from various connected sources, such as databases, sensors, or user interfaces. In this phase, it analyses text, images, or other formats to fully understand the initial situation.
• Reasoning: Agentic AI uses a large language model (LLM) to process the collected data, interpret the context, filter out what is truly valuable information, and formulate viable solutions. For example, if the goal of AI agents is to schedule a work meeting, the LLM analyses email content to identify participants, available time slots, and the purpose of the meeting. 
• Planning: Once the context has been understood, AI agents draw up a roadmap. This involves breaking the main objective into smaller subtasks and logically determining the most efficient path to complete them.
• Action: The system moves into execution and carries out the plan autonomously. At this point, AI agents interact with other software tools, make real-time decisions, or perform the automated processes required to achieve the set objective.
• Reflection: After executing the actions, AI agents critically evaluate the results to determine whether the objective has been successfully achieved. All the information gathered in this phase is used to correct deviations and refine their future plans. 

According to this view from Google, it is precisely this continuous cycle of perception, reasoning, planning, action, and reflection that gives AI agents the ability to learn, adapt, and improve their performance over time.

What is an agentic enterprise?

An agentic enterprise represents a structural shift in the way work is organised. It is not simply about incorporating artificial intelligence, but about designing a model in which human teams collaborate directly with AI agents.

What does this synergy involve? It allows artificial intelligence to take over repetitive, monotonous, and time-consuming tasks, while teams focus on higher-value areas such as strategy, creativity, and decision-making.

Companies that integrate agentic AI are characterised by:

• Autonomous execution of various processes: It independently handles complex and transactional processes, fully freeing up human teams.
• Real-time reasoning and decision-making: It interprets each user’s exact intent thanks to advanced LLMs to determine and execute the most accurate solution.
• Omnichannel experience with persistent context: If a customer starts an enquiry on WhatsApp and then continues via email, the AI retains the full context. This means the user does not have to repeat their information, significantly improving the overall experience.

How does agentic AI impact ecommerce? The rise of agentic commerce

Agentic AI is making a strong impact on digital marketing and ecommerce, transforming both the way users discover products and the way they purchase them. Its influence is already being reflected in industry forecasts, as a Juniper Research report estimates that the volume of agentic commerce will reach 1.5 trillion US dollars by 2030.

In parallel, data from IAB shows that the shift is already underway. In Spain, 13% of shoppers already use intelligent assistants in their purchasing process, and around a third of users turned to these tools to look for deals during the last Black Friday.

What is Agentic Commerce?

Agentic commerce defines a new scenario in which the user delegates the purchasing process to virtual assistants. It is no longer only about the visual design of an online store, but about the ability of AI agents to analyse, filter, and purchase products autonomously on behalf of the consumer.

The rise of agentic commerce has already led Google to launch the Universal Commerce Protocol (UCP). This initiative standardises market rules through tools for merchants and search formats adapted to Gemini. In this context, businesses are no longer competing for “clicks”, but for being the most selectable, transparent, and easily integrable option for virtual assistants.

Does it change the way users search? Searches are no longer made using generic terms such as “hiking trousers”; with agentic commerce, needs are now described in more detail: “I want hiking trousers that are comfortable, affordable, and waterproof.” Understanding this new ecosystem is a key step towards improving conversion rates through the automation of marketing strategies.

Why is Agentic Commerce crucial for your business?

Among the main benefits of agentic commerce are:

• Immediate purchase decisions: Assistants drastically reduce the consumer’s search time by directly suggesting ideal options based on their preferences and browsing history.
• Mass response capability: The system can process a huge volume of interactions and sales simultaneously, multiplying your store’s commercial capacity without overloading its resources.
• Instant tailored campaigns: It enables dynamic promotions, contextual offers, and exclusive bundles adapted in real time to each buyer’s exact needs.
• Seamless payment gateways: Cart abandonment is reduced as intelligent assistants can manage payments  securely, directly, and without friction.

At Redegal, we support brands through this transformation towards becoming agentic enterprises, helping them audit and optimise their platforms to successfully integrate into the agentic commerce ecosystem. We take care of structuring your product catalogues, ensuring real-time data synchronisation, and developing advanced solutions so your business is always the preferred choice for new consumers and their intelligent assistants. Shall we talk?