What is agentic commerce?

AI agents are artificial intelligence-powered systems that autonomously perform tasks by designing workflows with available tools. While simpler rule-based bots can respond to scripted prompts, modern intelligent agents have broader functionality—they can reason, plan and act across multiple systems and AI platforms.

Unlike traditional e-commerce experiences—which require a person to manually search for products, compare options, read reviews and complete checkout step by step—agentic commerce shifts much of that work to AI agents. In a traditional flow, shoppers must jump between tabs and retailers to evaluate choices and manually enter their information at checkout.

With agentic commerce, AI-powered shopping assistants proactively gather requirements, scan multiple retailers in real time, evaluate products against user preferences and constraints, and make purchases or recommendations on the user’s behalf. This streamlining the process.

Agentic commerce is not limited to online shopping. It is relevant to a wide range of commerce experiences, including things like travel and ticketing, subscriptions and digital services, and physical retail integrations.

Agentic commerce is part of the next phase of integrating generative AI (gen AI) into commerce. A 2026 IBM Institute for Business Value study found that 45% of consumers already use AI for part of the buying journey.

The use spans from interpreting reviews to hunting for deals, indicating that consumer habits are shifting toward AI-shaped purchasing decisions. Other research suggests that agentic commerce could generate between USD 3 trillion and 5 trillion globally by 2030.¹

The current wave is being shaped by gen AI advances and tooling ecosystems—from OpenAI models used inside assistants to retail integrations that increasingly touch marketplaces like Amazon.

Earlier generations of retail AI—such as recommendation engines or chatbots—were reactive and required step-by-step human prompting. Modern agentic AI agents differ in three ways:

• Autonomy: They can act without constant user inputs, following predefined frameworks and guardrails.
• Reasoning: They adapt recommendations and actions based on evolving conditions, such as price changes or stock depletion.
• Interoperability: They integrate into many AI platforms and workflows through open APIs and open source connectors, enabling automation across multiple systems.

While earlier commerce AI was limited to responding to queries and making static product suggestions, today’s agents can operate as shopping assistants, shopping agents or merchant agents. They can be embedded into applications like ChatGPT, Gemini or Perplexity. Through natural language interaction, they match query intent to structured product data and manage payments and other tasks across e-commerce platforms and physical retail systems.

These shopping agents don’t just recommend a pair of shoes; they navigate e-commerce platforms, compare the price across multiple retailers, apply coupons and complete purchases by using pre-authorized agentic payments methods.

As agentic commerce continues to evolve, so too will consumer behaviors and expectations. Today, customers are used to going to a specific site or platform to seek out particular products or services. But agentic commerce blurs those lines and makes those same products and services accessible for purchase through other means.

For example, a consumer might need to reorder a household item, book a hotel or renew a subscription. In a traditional model, they would visit one or more websites to complete these tasks. With agentic commerce, they can instead ask an AI agent to help. The agent completes the transaction through a conversational interface or a connected service. The user does not need to visit the retailer’s website or app.

Adoption is also accelerating for both businesses and consumers. Many startups now offer deployable components for agent orchestration, evaluation and governance. These components are often built on open source frameworks for easier use.

Agentic commerce typically flows through several stages, connecting human input with independent AI action:

At the center of agentic commerce is the user-to-agent relationship. Users define goals, permissions and constraints, such as budget limits or brand preferences. For example, a consumer might prompt an AI agent, “Find me a camping tent under USD 150 and have it delivered by Friday.” The shopping agent interprets the request, accesses structured product data and applies filters based on price, specifications and delivery availability.

Done correctly, this interaction might feel less like form-filling and more like a guided conversation that improves the user experience while respecting permissions and constraints.

Agentic AI goes beyond standard AI tools by planning multistep workflows, calling external APIs and adjusting actions along the way. This complexity allows for autonomous actions such as monitoring price changes in real time, reordering inventory when it runs low and completing purchases without repeated human approval. Autonomy is usually tiered so that low-risk purchases are fully automated while high-priced or sensitive purchases might still require human approval.

In the previous camping tent example, the authorized agent searches multiple retailer databases across different providers to compare deals in real time. It might also use an agent-to-agent protocol to negotiate extras, such as bundled items or loyalty discounts.

Agentic commerce makes the product discovery process less about searching or browsing and more about achieving a specific goal. Agents analyze product data from multiple sources. They compare factors like price, availability, delivery time and reviews.

As agentic capabilities evolve, these processes are increasingly multimodal, meaning they incorporate text, images, user history and structured data. This development is driving interest in generative engine optimization (GEO), which focuses on structuring product content so that LLMs and agents can interpret it. Instead of optimizing only for human searches, brands now need machine-readable product data, standardized attributes and clear metadata so AI systems can discover and use it.

For agentic commerce to operate at scale, retailers and service providers must make their systems accessible through machine-readable interfaces. This accessibility typically involves exposing APIs for product catalogs, pricing and real-time availability, along with return policies, warranties and other information.

These interfaces enable merchant-to-agent communication so that AI agents can validate inventory and execute purchases on a user’s behalf. Increasingly, this integration is discussed in terms of emerging or proposed standards—often referred to as an Agentic Commerce Protocol (ACP). These standards aim to define how AI agents and merchants exchange this kind of structured information.

Agentic payments are a key part of the agentic commerce process. In recent years, major e-commerce platforms and payment providers have expanded API capabilities to support automated purchasing workflows and subscription management. Agentic purchases are completed by using delegated authentication systems, such as Google’s Agent Payments Protocol (AP2), Visa’s AI-ready tokenized credentials or Stripe’s integration with instant in-app checkout in ChatGPT. These authentication systems allow for transaction transparency and provide audit trails to support fraud detection.

Once they have completed a purchase, agents might take on other tasks, such as tracking shipments and managing returns. They can also initiate after-market product recommendations for accessories or complementary goods to go beyond the initial sale.

Agentic commerce offers many benefits, including:

• Faster and more efficient transaction processing
• The ability to scale purchasing activity across large volumes
• Real-time personalization of offers
• Reduced checkout friction through agentic payments
• Better operational accuracy with less manual oversight.

AI agents can help consumers reduce search time and speed up decision-making by delivering personalized product recommendations based on purchasing history and preferences. For businesses, agentic commerce offers new paths to product discovery and the potential to monetize agent interactions through targeted offers or bundled deals.

Agentic commerce faces some barriers to adoption, including:

• Data readiness: Some retailers struggle with fragmented product data across systems, which limits discoverability and interoperability.
• Trust: IBM’s research shows that 83% of consumers share overlapping worries about privacy, data misuse and unsolicited marketing. And some customers still prefer manual transactions to automated ones.
• Legacy adaptation: Existing fraud and payment systems were built for human verification, not machine intermediaries. Businesses that want to adopt agentic experiences will need to update.

While agentic commerce is often discussed in the context of online shopping, its applications extend to any section in which purchasing and transactions are complex, repetitive or time-sensitive. As AI agents gain the ability to interact directly with vendors and payment systems, agentic commerce is being explored as a coordination layer for a wide range of commercial activities.

Enterprises can use agentic commerce to automate procurement decisions, especially for routine or low-risk purchases. AI agents can validate approved vendors, negotiate volume-based pricing and place orders. In supply chain contexts, agents might also respond to inventory requirements or disruptions by sourcing alternative suppliers in real time.

Some 61% of procurement leaders cite geopolitical and supply risks as top concerns, and by 2028 half of G2000 manufacturers are expected to operationalize AI‑enabled circular supply chains. Agentic commerce provides the transaction and coordination layer that makes that a reality.

In retail, agentic commerce enables AI agents to manage recurring purchases, compare prices across multiple retailers in real time and place orders based on user-defined preferences or constraints. Agents might coordinate online ordering with in-store pickup or local delivery, reducing friction across channels. For retailers, this functionality might shift competition toward machine-readable product data, availability and fulfillment reliability, rather than solely brand visibility.

Agentic commerce is increasingly applied to the management of digital subscriptions, licenses and usage-based services. AI agents can monitor how much the subscription is being used, cancel underused services, upgrade plans when thresholds are reached or switch providers based on price or performance criteria. This use case focuses on post-purchase optimization more than initial discovery.

In travel and hospitality, agentic commerce supports end-to-end booking workflows, including flights, accommodations, ground transportation and other elements. AI agents can monitor prices, rebook trips when conditions change and handle refunds or credits automatically, within predefined approval limits.

Organizations that want to embrace agentic commerce need to:

1. Standardize product data so that it is both human- and machine-readable.
2. Integrate open APIs to allow seamless cross-agent interactions.
3. Apply clear guardrails to uphold trust and compliance.
4. Rethink SEO for AI so agents, not just humans, can find and act on offers.

Early adopters of agentic commerce will be able to influence how these intelligent AI systems handle discovery, recommendation and loyalty within the ecosystem.