Meta has launched Muse, an artificial intelligence assistant designed to carry out practical tasks on behalf of users: from cleaning out an inbox to online shopping and travel planning. It is a significant shift for a company that, until now, has tied much of its AI strategy to social media, conversation, and content creation. With Muse, Meta is attempting to enter the realm of productivity tools capable of taking concrete action across external services.
The difference is not marginal. A chatbot can suggest how to organize an inbox or point out which products to search for; an agent, on the other hand, must be able to view the inbox, navigate websites, decide what to delete, and proceed all the way to confirming a purchase. It is a level of delegation that promises to save time, but also requires opening up to Meta information and accounts that many users consider among their most sensitive.
A review published by The Verge reflects precisely this dual impression. Across various tasks, Muse managed to accomplish what it was asked to do. At the same time, the volume of personal data it can collect and use to operate left the journalist who tested it with a strong sense of unease. It is therefore not just a matter of technical accuracy: trust becomes an integral part of the product.
An agent operating from a cloud computer
Muse executes actions via a virtual computer hosted in the cloud. In practice, the assistant can navigate the interfaces of web services and complete sequences of operations that would normally require direct human intervention. Meta presents this setup as a way to eliminate repetitive chores: email, shopping, scheduling, and other routine online tasks.
In the test, one of the first requests involved Gmail: finding and deleting unnecessary messages. To do so, users must connect their Google account and grant Muse permission to access, read, and delete emails. The experience was not seamless on a smartphone, where the Google sign-in screen ran into issues and repeatedly redirected back to the Muse website. The connection was subsequently successful on a laptop, and the assistant deleted thousands of promotional emails and updates.
The practical result, therefore, has arrived. But the case also highlights the nature of the trade-off involved. To distinguish between messages to delete and important communications, a system of this type needs a very broad view of one's correspondence. Delegating inbox cleanup is not the same as using a spam filter: it means allowing an external agent into a space that often contains receipts, private conversations, work data, bookings, and financial information.
Meta states that Muse exchanges with third-party applications only the data necessary for them to function on the user's behalf, and that this information is not shared with advertisers. These are crucial commitments, as the company carries a history of controversy and distrust around privacy management. For anyone evaluating Muse, the question will not only be which permissions appear upon linking, but also how understandable, revocable, and proportionate they are to the requested action.
From email to shopping: when the assistant takes initiative
The test continued with Amazon. After linking the account, Muse received precise instructions to select workout tank tops based on size, style, and colors. Before proceeding with the order, the agent noticed that there were other items in the cart and asked if they should be removed. Having received authorization, it purchased only the requested garments.
This behavior is noteworthy because it goes beyond a simple product search. Muse recognized a potentially ambiguous situation—items already in the cart, not necessarily related to the new task—and paused the automation to ask for a decision. In an agent capable of moving money or modifying data, these confirmation steps are not mere interface details: they are one of the few barriers between a helpful action and an error with tangible consequences.
However, the question of scope remains open. An e-commerce account contains past purchases, delivery addresses, preferences, and, in some cases, payment methods. Even when a task is narrow, the assistant may have access to far more context than needed to carry it out. Meta's stated principle on data minimization will therefore have to be evaluated in practice, not just through the service's broad promise.
Productivity, content, and already visible limits
Muse’s capabilities go beyond operational browsing. The assistant can generate AI podcasts, images, and videos, as well as create web pages or interactive documents that Meta calls “artifacts”. The package therefore brings together features that are currently often scattered across chatbots, multimedia generators, and document creation tools.
The test also revealed the limits applied to image generation. Muse rejected prompts that recognizably described protected cartoon characters, such as a mouse with red pants and a character with a red hat and blue overalls. This signals the restrictions built into the product for requests that come too close to well-known intellectual property. On its own, it does not reveal how consistent or effective these safeguards are in every scenario, but it confirms that Meta has set explicit boundaries in this area as well.
The Verge’s test does not amount to a comprehensive assessment of Muse’s reliability across every scenario. However, it shows a product capable of completing real-world tasks, albeit with some technical friction, and brings the category’s most delicate issue into focus: the greater an assistant’s autonomy, the broader the access and context granted must be to make it truly useful.
The challenge for Meta is turning permissions into trust
For Meta, Muse represents an attempt to move AI from features integrated into the social experience toward a role closer to that of a personal assistant. The stakes are high: if agents become the standard way users interact with online services, stores, and documents, whoever controls the assistant will hold a far more central position than someone merely offering a feed or a chatbot.
Yet productivity leaves little room for mishaps. A wrong suggestion can be ignored; a deleted email, a confirmed purchase, or a misinterpreted private detail can have an immediate impact. Muse will therefore have to prove not only that it can act, but that it can clearly communicate what it is looking at, what action it is about to perform, and when waiting for human approval is essential.
For users, the most prudent approach will be to start with low-risk tasks and carefully check the permissions granted by individual connections. The convenience demonstrated in testing is tangible, especially when managing repetitive tasks. However, in Meta's case, the agent's effectiveness is not enough to dispel reservations: the value of Muse will depend on the company's ability to make that operational power compatible with credible control over personal data.
