Agentic commerce is emerging as the next major shift in digital retail - one that changes how customers discover products, make decisions, and complete purchases.

For ecommerce brands, this isn’t just a UX evolution. It’s a fundamental change in how demand is accessed, influenced, and won, with AI agents increasingly acting as the decision-maker between shoppers and storefronts.

What is agentic commerce?

Agentic commerce refers to a model where AI agents interpret intent, gather product data, compare options, and transact on the behalf of humans.

From a retailer’s perspective, seller agents replace the traditional consumer journey, still leading to the final purchase.

Commerce is shifting from human-to-website interactions to agent-to-system and agent-to-agent transactions - fundamentally changing how products are discovered and chosen.

What Exactly Is an AI Agent?

An AI agent is more than a chatbot. It combines reasoning, memory, and action to complete tasks end-to-end. At a high level, agents are made up of four core components:

Reasoning (LLMs): Large language models interpret intent, plan steps, and make decisions, but rely on external systems for live data.
Memory: Agents retain short-term context (current task) and long-term preferences (past behaviour), enabling personalisation.
Instructions: Rules and constraints define how an agent behaves, such as spending limits, negotiation style, or risk tolerance.
Tools: APIs and integrations allow agents to check stock, compare prices, place orders, and communicate with other agents.

Together, these components allow agents to operate independently while staying aligned with user or business goals.

How Agents Communicate and Transact

For agentic commerce to work at scale, shared standards are emerging to define how agents interact.

Model Context Protocol (MCP): Enables agents to reliably connect to tools and APIs without custom integrations.
Agent-to-Agent (A2A): Allows agents to identify themselves, share capabilities, and communicate directly.
Agent Payments Protocol (AP2): Handles secure authorisation, payment, and transaction records between parties.

These protocols form the technical foundation for agent-driven discovery, negotiation, and payment.

What This Means for Shopping Experiences

In practice, agentic commerce simplifies the buying journey. A single conversation can replace multiple tabs, manual comparisons, and checkout steps. Agents handle discovery, pricing, purchase, and post-purchase actions such as delivery tracking or returns.

For example, a customer might ask their AI agent to “find a waterproof running jacket under £150 that can arrive before the weekend.” The agent evaluates multiple retailers, compares stock availability, delivery speed, return policies, and price - and completes the purchase without the customer ever visiting a product page.

For retailers, this means customer experience is no longer defined solely by on-site UX. Visibility to agents, data accessibility, and system interoperability become just as important as design and merchandising.

Agentic Workers: AI Inside the Business

Agentic AI doesn’t only affect customers. Retailers are increasingly deploying internal AI agents or “agentic workers” to automate operational tasks. Common use cases include:

Operations and inventory

Real-time inventory allocation across channels
Dynamic pricing within defined parameters
Automated order issue resolution
Fraud detection and risk management

Marketing and merchandising

Automated campaign launches
Personalised promotions at scale
Real-time content and metadata updates
Contextual messaging based on customer behaviour

These internal agents help retailers operate faster, reduce costs, and respond more dynamically to demand.

How Retailers Can Prepare Now

Retailers don’t need to wait for agentic commerce to fully mature. Practical preparation can begin today:

1. Adopt API-first architecture

Ensure systems are accessible programmatically so agents can interact with product, pricing, and inventory data.

2. Make services agent-readable

Standardise APIs and documentation so AI agents can reliably understand and use them.

3. Start with internal automation

Deploy agentic workers behind the scenes for pricing, inventory, or content updates to build confidence and capability.

4. Develop a seller agent

Begin with basic product queries and availability, then expand into negotiation and checkout over time.

5. Simulate agent-driven scenarios

Test how your systems respond to agent-led transactions before real agent traffic arrives.

Final Thoughts

As agentic commerce scales, retailers won’t compete solely on branding or UX - they’ll compete on how clearly, reliably, and intelligently their systems can be understood by AI agents.

From an ecommerce agency perspective, agentic commerce shifts the focus from designing pages for humans to designing systems for machines - without losing brand control or commercial strategy.

Brands that invest early will shape how agents evaluate them. Those that don’t risk becoming invisible in a market where machines increasingly decide what gets bought.

Download The Agentic Commerce Playbook here