Meta has introduced Muse, a personal AI agent designed to do more than answer questions. It can browse the web, work across connected services, complete multi-step tasks and even make purchases on a user’s behalf.
Unlike a conventional chatbot that waits for individual prompts, Muse can continue working in the background after a user leaves the app. A person could ask it to organize a trip, manage a project or find and purchase a product, with the agent carrying out the steps needed to complete the task.
AI has already become part of online shopping through conversational search, product recommendations and comparison tools. Muse goes further by allowing the agent to act on the information it finds.
Meta says Muse can browse websites and connect with services such as email, calendars and Instagram. It can also learn from previous conversations and use that context when suggesting what a person might need.
When an action is sensitive, however, Muse is designed to return control to the user. For example, before sending an email or completing a purchase, the agent asks for approval. Users can also see an audit trail showing what Muse has done and what it plans to do.
Shopping is supported through Stripe’s Link wallet. At more than one million businesses that accept Link, Muse can use a consumer’s preferred payment method. For other businesses, Link can generate a single-use virtual card for an approved transaction. The consumer sees and approves the total in the Muse interface before completing the purchase.
This means the AI is no longer limited to saying where someone might buy a product. It can potentially navigate the purchasing process itself.
This changes an assumption that has shaped e-commerce for decades: that the person viewing the product page is also the person interpreting all of the information on it.
An AI agent may approach shopping differently.
Instead of scrolling through several product pages, a user could ask Muse to find a laptop within a particular budget, compare battery life and weight, check compatibility with certain software and purchase the most suitable option.
To complete that request accurately, the agent needs reliable information about the products it encounters.
Price and availability matter, but so do identifiers, technical specifications, dimensions, compatibility, product relationships, warranty information and other attributes. When those details are incomplete or inconsistent, an agent has less reliable information to compare products and act on the shopper’s instructions.
Product data quality therefore becomes part of the infrastructure behind agentic commerce.
This is where Muse is particularly relevant to Icecat’s ecosystem.
Icecat standardizes product information so brands, distributors, retailers and marketplaces can exchange and reuse product content across channels. In an agent-driven shopping environment, those structured product records can also help AI systems understand what a product is and how it differs from alternatives.
The difference becomes important at scale.
A human shopper might tolerate opening several tabs to find that one retailer lists battery capacity in one format while another buries it in a description. An AI agent comparing hundreds of potential products needs information it can interpret consistently.
Structured attributes make characteristics easier to compare, while standardized identifiers help distinguish similar models and variants. Complete product information also reduces the need for an agent to infer details that the seller has not clearly provided.
The product catalog is therefore no longer serving only the retailer’s website or marketplace listing. It can become a source of information for another layer of software deciding which products deserve consideration.
Meta also has something that many AI companies do not: an enormous existing consumer ecosystem.
Muse is initially rolling out in the United States through iOS, Android and the web, and users can also interact with it directly through WhatsApp. Meta says support for its AI glasses is coming soon.
That distribution could make agentic behavior more familiar to consumers who would never deliberately seek out a specialized AI shopping tool.
Muse can also connect with Instagram when users grant permission. This creates an interesting connection between product inspiration and action. A user might discover an idea through social content, discuss it with an AI agent and eventually allow that agent to handle parts of the purchasing process.
Meta gives the example of Muse turning a recipe Reel into a grocery list and then helping coordinate the associated tasks. The same principle could extend across many product categories.
Giving an AI permission to browse is one thing. Allowing it to spend money is another.
Meta has therefore put considerable emphasis on security and user control. Muse operates inside what the company calls Muse Secure VM, a dedicated virtual machine with its own browser. Users decide which services the agent can access and can revoke that access. Meta also says Muse conversations and VM data are not shared with its advertising systems.
Whether consumers become comfortable delegating meaningful shopping decisions to agents remains an open question.
But Muse makes the e-commerce implications more concrete. AI assistants are moving beyond recommending products toward navigating websites, comparing options and participating directly in transactions.
For brands and retailers, that means product content may increasingly need to work for two audiences at once: the person ultimately making the decision and the AI agent helping that person make it.
As agents become capable of taking more steps between “I need something” and “buy it,” accurate, structured and machine-readable product information becomes increasingly important to making those decisions work.
Read further: News, AI, e-commerce, ecommerce, Icecat, Meta, Muse, product content
