Agentic Commerce refers to the use of AI agents that can independently make purchasing decisions and carry out purchasing processes on behalf of customers or companies.
Unlike with traditional online stores, users no longer search for products themselves, compare offers, or manually go through the ordering process.
Instead, an AI agent handles these tasks largely on its own. The user simply formulates a goal or a request, while the AI plans and executes the necessary steps.
For example, a user can instruct an AI agent to book the cheapest flight for a business trip. The agent then researches suitable offers, compares prices, takes individual specifications into account, and can complete the entire purchase process.
Agentic Commerce thus goes far beyond traditional product recommendations or chatbots.
While conventional systems merely provide support, AI agents act proactively and make decisions on their own within defined limits. In this way, they become digital representatives of the customer or company.
For companies, this changes the way their products or services are discovered, evaluated, and purchased.
To be ready for Agentic Commerce, offers must be formatted for AI agents. Product data, prices, availability, and delivery terms thus become key competitive factors.
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“Agentic” means that an AI system not only provides information but also acts independently and prepares decisions.
The term is derived from the English word “agent.” In the context of artificial intelligence, it describes systems that can understand goals and plan actions. Instead of merely providing answers to queries, AI agents take action and carry out tasks independently.
Agentic Commerce works by having AI agents independently plan, coordinate, and execute purchasing processes to achieve a goal specified by the user.
A typical process in Agentic Commerce proceeds as follows:
The difference is that in e-commerce, the user controls the entire purchasing process; in conversational commerce, AI provides support; and in agentic commerce, AI carries out many steps on its own.
In a sense, these three approaches build on one another. In the process, the role of technology shifts from a mere sales channel, through a digital assistant, to an active AI agent that acts on behalf of the user.
The differences at a glance:
| Characteristic | E-Commerce | Conversational Commerce | Agentic Commerce |
|---|---|---|---|
| Who is looking for products? | Customer | AI-Assisted Customer | AI agent |
| Who compares offers? | Customer | Customer | AI agent |
| Who makes purchasing decisions? | Customer | Customer | AI agent within predefined rules |
| Who will fulfill the order? | Customer | Customer | AI agent or customer |
| Interaction | Website or App | Dialogue with AI | Giving objectives/instructions to an AI agent |
| Degree of automation | Low | Middle | High |
Agentic Commerce is gaining importance because AI agents are now capable of carrying out purchasing processes largely on their own.
For companies, this offers opportunities for faster procurement, lower transaction costs, and greater automation.
At the same time, there is a growing need for high-quality data, well-maintained product information, and digital interfaces so that AI agents can find and select offers.
Agentic Commerce can help companies streamline their purchasing and sales processes.
AI agents handle tasks such as:
This frees employees from routine tasks, allowing them to focus more on strategic activities.
At the same time, AI agents support faster and more informed decision-making. They analyze large amounts of data, compare suppliers, and take into account factors such as prices, availability, delivery times, and company policies.
This helps accelerate recurring procurement processes, reduce errors, and lower costs.
Another advantage lies in scalability and personalization. AI agents can support many purchasing decisions simultaneously without a corresponding increase in staffing requirements.
At the same time, they take individual customer preferences into account, enabling the presentation of more relevant offers.
The biggest challenge with agentic commerce is that AI agents can only be as reliable as the data, processes, and systems they access.
For AI agents to evaluate products correctly and make purchasing decisions, they need up-to-date, complete, and consistent information.
The following issues, for example, can lead to incorrect recommendations or incorrect orders:
For companies, the quality of their product data therefore becomes a critical competitive factor.
Another challenge is controlling decisions and processes. Companies must clearly define which tasks an AI agent is permitted to perform independently. It must be clear at which points human approval is required.
Transparent decision-making rules and governance structures are indispensable, especially for major investments, sensitive procurements, or regulated industries.
Integrating existing systems also poses challenges for many companies. AI agents often need to access online stores, ERP systems, and external data sources. Legacy IT landscapes often complicate this integration and can slow down automation.
Common mistakes in the field of agentic commerce include, for example:
Agentic Commerce can help companies partially or fully automate purchasing, sales, and service processes using AI agents. Use cases involving high data volumes, recurring decisions, and standardized workflows are particularly interesting.
Examples of use cases with high value include:
In these areas, AI agents can accelerate processes, reduce costs, and relieve employees of routine tasks. This makes Agentic Commerce a strategic lever for efficiency and automation for companies.
Companies can prepare for Agentic Commerce by first improving their data quality, processes, and digital interfaces.
AI agents can only make reliable decisions if product data, prices, availability, and master data are complete, up-to-date, and consistent.
For many companies, preparation therefore begins with creating a clean database and standardized processes.
In addition, companies should open up their system landscape to work with AI agents. This includes integrating online stores, marketplaces, ERP systems, customer platforms, and other business applications.
At the same time, clear rules must be defined. Companies must specify which decisions an AI agent is allowed to make independently and when human approval is required. Governance, data protection, and security become key success factors in this process.
Another important step is the gradual introduction of specific use cases. Instead of aiming for full automation right away, companies should first identify processes with a high degree of repetition and clear rules.
In this way, companies can gain experience and reduce risks. Management can thus gradually prepare the organization for a future in which AI agents increasingly support or independently execute purchasing and procurement processes.
SAP positions itself as a provider of technology platforms and AI agents that companies can use for agentic commerce scenarios.
SAP offers various features in the area of agentic commerce. These enable AI agents, for example, to find products, evaluate offers, coordinate purchasing processes, and automate business workflows. SAP users can access these capabilities through Joule, Joule Agents, the SAP Business Technology Platform, and solutions such as SAP Commerce Cloud.
SAP’s goal is for AI agents to not only provide information but also to independently execute multi-step processes. In doing so, they access business data, corporate rules, and process context.
Agentic Commerce is expected to evolve from pilot projects into an integral part of digital purchasing and sales processes.
Especially in standardized purchasing processes, AI agents will in the future research products, compare offers, place orders, and monitor transactions.
It will become increasingly important for companies to make product data, prices, availability, and interfaces available in a way that allows AI agents to use them. Competition is thus increasingly shifting toward being found, evaluated, and selected by AI agents.
