What Is Agentic Commerce?
Definition: What Agentic Commerce Means
Agentic commerce is a form of e-commerce in which autonomous AI agents independently execute tasks such as product research, price comparison, and purchase completion, without the user having to manually control every step.
The term combines „agentic“ (goal-directed, acting independently) and „commerce“. At its core, it describes the transition from reactive systems that wait for commands to proactive systems that pursue goals, make decisions, and complete tasks autonomously.
This sets agentic commerce fundamentally apart from classical AI applications in e-commerce. A recommendation algorithm suggests products, the human decides. A chatbot answers questions, the human searches on. An AI agent, however, receives a goal – such as „find running shoes under €150 that can be delivered by Friday“ – and independently executes all necessary steps: searching, comparing, filtering, buying. Mastercard describes this as the end of manual „tab-hopping“ in online shopping.
How AI Agents Work in Commerce
AI agents in commerce operate in a four-step cycle: perceive, plan, act, learn.
The agent receives a goal. It breaks it down into sub-steps and selects the necessary tools: APIs, search functions, product databases, payment systems. It then executes actions iteratively and self-correctively. Based on the results, it improves its future decisions.
Technically, agentic commerce is built on large language models (LLMs) connected to external tools and data sources. Standardized protocols such as the Model Context Protocol (MCP) by Anthropic enable agents to communicate with merchant systems, retrieve product data, and execute transactions.
The key point: an AI agent is only as good as the data it can access. Structured, complete, and consistent product data is the prerequisite for an agent making correct decisions.
Two Perspectives: Buyer Side and Seller Side
Agentic commerce can be viewed from two perspectives that are mutually dependent.
Buyer side
AI agents act on behalf of consumers. They receive an assignment, research across multiple retailers and platforms, compare prices, delivery times, and reviews, and complete the purchase independently. According to Mastercard, this marks the end of manual tab-hopping: the buyer states a goal, the agent handles the rest.
Seller side
AI agents take over commerce tasks within the company itself. They generate channel-specific product descriptions, identify data gaps in the PIM and suggest additions, dynamically adjust prices, monitor availability, and manage content delivery across channels. For merchants, this side of agentic commerce is often more immediately relevant, as it directly intervenes in existing processes and enables immediate efficiency gains.
Practical Use Cases Today
Agentic commerce is not a future concept. Several use cases are already in productive use:
- AI-powered shopping assistants: Platforms such as Perplexity, ChatGPT Shopping, and Google Shopping Graph allow users to find products through natural language queries. Agents search product catalogs in real time, filter by preferences, and deliver direct purchase recommendations.
- Autonomous content generation: Agents create product descriptions in multiple languages, adapt them channel-specifically, and update them automatically when products change.
- Data enrichment in PIM: Agents identify missing mandatory attributes, suggest additions, and enrich product records based on similar entries.
- Price optimization: Agents monitor competitor prices and demand signals, and adjust prices independently within defined guardrails.
- Agentic checkout: Initial implementations allow agents to execute the complete checkout process, including payment processing via standardized protocols.
Why Agentic Commerce Depends on Data Quality
Whether an AI agent delivers useful results or produces inaccurate outputs depends almost entirely on the quality of the product data it accesses.
Salesforce puts it precisely: „an agent is only as good as the data it can access.“ Fragmented product information, inconsistent attributes, missing media assignments, or isolated data silos lead agents to make wrong decisions, provide unsuitable recommendations, or complete transactions on an incorrect basis.
For merchants, this means: anyone who wants to benefit from agentic commerce must consistently maintain their product data foundation. A central PIM system that structures, connects, and delivers product data channel-wide via API is the technical prerequisite for AI agents to work productively and reliably. What such a data foundation must specifically deliver is explained in our article KI-ready or not?
Market and Outlook
Agentic commerce is developing faster than most previous AI applications. According to Adobe Analytics, AI-driven traffic to U.S. retail websites grew by 4,700 percent between July 2024 and July 2025. The McKinsey study from October 2025 estimates that AI agents could mediate purchases worth $3 to $5 trillion in global consumer commerce by 2030. According to Morgan Stanley, 23 percent of Americans have already made a purchase via an AI agent in the past month.
Gartner forecasts that 33 percent of enterprise software applications will include agentic AI by 2028, up from less than 1 percent in 2024. At the same time, Gartner warns that over 40 percent of agentic AI projects will be canceled by the end of 2027 due to underestimated costs, unclear business value, and inadequate governance. For merchants, this means: the technology is real and the potential is significant, but success depends on concrete value and solid data foundations.
Agentic commerce is a form of e-commerce in which autonomous AI agents independently execute tasks such as product research, price comparison, and purchase completion. Unlike classic automation, agents act in a goal-directed manner, make independent decisions, and complete multi-step tasks without manual intervention.
In traditional e-commerce, the person actively navigates the purchase process: searching, comparing, adding to cart, and checking out. In agentic commerce, an AI agent takes over these steps based on a stated goal. The difference lies not in the technology but in the autonomy: the agent acts independently, not reactively.
According to Morgan Stanley, AI agents could mediate up to $385 billion in U.S. e-commerce by 2030. Adobe Analytics recorded a growth of 4,700 percent in AI-powered shopping search queries on U.S. retail websites between July 2024 and July 2025.
On the merchant side, agentic commerce primarily requires clean, structured, and API-accessible product data. Fragmented data silos and inconsistent attributes lead to agents making incorrect decisions. A centralized PIM system forms the technical foundation.
A chatbot answers questions and responds to inputs without independently pursuing goals or executing multi-step tasks. An AI agent receives a goal, breaks it into sub-tasks, selects and uses tools, and executes actions independently, even without further human input.
