Until now, most people have encountered artificial intelligence through an empty chat box: ask a question, receive an answer, check it and then take the next step yourself. Meta wants to change precisely that final stage. Its new Muse agent is designed not merely to talk, but to do work on a person’s behalf.
Muse can send an email, find and book a trip, make a restaurant reservation, buy a product or carry out longer assignments using third-party apps and websites. Users can interact with it through a dedicated Muse app or directly through WhatsApp.
The product is launching first in the United States and is available only to adults. Basic use is free but limited, while higher usage tiers cost $20 and $100 a month. Users can also give the agent a custom name and avatar.
More important than the pricing, however, is the operating model. Muse can connect to services people already use — from Gmail and OpenTable to Ticketmaster, Shopify, Spotify and other platforms. Access to Facebook and Instagram also gives Meta additional user context with which to personalize the assistant.
Daycom’s analysis indicates that Muse marks an important shift from AI that knows about your life to AI that is allowed to change something in it. The difference between recommending “here is a good restaurant” and actually completing the reservation may seem small, but technologically and psychologically it represents an entirely different level of trust.
With a conventional chatbot, a wrong answer usually remains text on a screen. An agent’s mistake can become a sent email, the wrong ticket purchase, an unwanted order or an action taken inside a user account. The next phase of AI will therefore be judged not only by the quality of its answers, but by how safely it can act.
Meta describes Muse as a personal agent that can be given not only a single instruction, but a broader goal. The system can help break that goal into steps, coordinate time and resources, and then continue working on its own without requiring a separate instruction from the user for every subsequent action.
For example, an Instagram video saved for its recipe can become the basis for a Muse-generated shopping list. The agent can take into account dietary restrictions previously mentioned by friends, suggest a dinner menu and help organize the next steps. That memory of context is what is meant to turn it from a general chatbot into a personal assistant.
Travel offers another example. Instead of opening dozens of tabs for flights, hotels, calendars and messages, a user can describe the desired outcome. The agent is expected to gather information, compare options, work with booking services and return to the user when a final decision is required.
A similar logic applies to restaurants and tickets. Once OpenTable or Ticketmaster is connected, Muse can notice events or available reservations that match a user’s interests and suggest taking action. In some scenarios, the distance between a recommendation and completing the booking is meant to be reduced to a single click.
With Shopify and payment infrastructure, the agent gains another fundamentally new capability: spending money on a person’s behalf. At that point, accuracy stops being only a technical issue. An AI mistake can carry a direct financial cost, which means confirmation, refund and accountability mechanisms become essential.
Meta says Muse operates inside a dedicated cloud environment called Muse Secure VM — effectively a separate protected computer with its own browser. The agent’s environment and the data from connected services are stored there, isolated from the corresponding environments of other users.
A separate agent called Sentinel operates within that system. It is isolated from Muse at the system level and monitors its external actions. Meta says an operation should not reach the internet without the appropriate checks, and when human consent is required, the system is designed to ask for it.
The company also says Muse does not directly see users’ passwords or payment credentials. Account information is stored separately, while the agent is given the ability to use those credentials for an authorized action without reading the password in plain text. More sensitive actions require additional confirmation.
Users are meant to decide which applications Muse can access and what permissions it receives. Email access, for instance, may mean permission only to read messages or also to send them. Permissions can be changed or revoked, while the agent’s activity history is intended to show what it has already done and what it plans to do next.
Meta separately says conversations with Muse and data from its virtual machine are not passed to the company’s advertising systems. Users can also opt out of having their interactions with the agent used to train models and can ask Muse to forget specific information it has remembered.
For Meta, that is an especially sensitive promise. A personal agent can, by its nature, know much more than a conventional social network: correspondence, calendars, purchases, upcoming trips, financial actions, favorite places, contacts and habits. The more useful the agent becomes, the more context a user must be willing to expose to it.
That is why the biggest obstacle for Muse may not be whether the model can perform a task, but whether millions of people are willing to grant it enough access. A social network can function even if a user refuses access to contacts. An agent asked to organize a dinner or business trip quickly loses much of its value without that kind of context.
Meta acknowledges that the agent can make mistakes. During Muse’s development, safety became complicated enough that the company had to build a separate control architecture for a system that works in the background, uses tools and gains access to email, calendars and other sensitive services.
Those risks are not theoretical. Internal testing uncovered problems involving reliability and the handling of sensitive information, and the product’s launch had to be delayed for additional security work. Meta says the current version has reached the level of safety required for release.
Prompt injection is particularly dangerous for agent systems — hidden instructions embedded in a webpage, document or message. A conventional chatbot might respond strangely after encountering such content. An agent could theoretically take an unwanted external action, which means protection has to work before the action is carried out.
Muse Spark, the model powering the agent, was specifically trained to use tools, handle long sequences of actions and coordinate multiple agents. Meta has already updated the family to Muse Spark 1.3, emphasizing long-running agentic workflows and the ability to revise plans as new information appears.
Behind that technical work lies a much larger bet by Mark Zuckerberg. Meta is spending enormous sums on models, computing infrastructure and data centers in an effort to transform artificial intelligence from a feature inside Facebook or Instagram into a standalone platform.
Muse is one of the first major consumer products from Meta Superintelligence Labs, the organization through which the company is trying to close the gap with the strongest AI laboratories. Zuckerberg has described the ultimate goal more broadly: a personal “superintelligence” system that works continuously on a user’s behalf.
In that sense, WhatsApp may prove just as important as the model itself. For mass adoption, an agent does not have to persuade people to learn a complex new interface. It only has to become another conversation inside an app where the user already sends messages every day.
The next step is expected to be Meta’s smart glasses. The company plans to integrate Muse with devices equipped with cameras and voice interfaces. That would give the agent another kind of context — not only what a person writes or saves online, but also part of what they see around them.
This is where the idea of a personal agent begins to resemble a permanent digital companion. It knows the calendar, sees part of a user’s communications, remembers preferences, has access to services, can act online and could eventually receive visual context through glasses. Its usefulness and the scale of its access increase together.
Meta has also announced an even more protected architecture, Muse Confidential VM, which it plans to introduce later this year. Under that model, data and conversations would be encrypted with a key accessible only to the user, so that even Meta, according to the company’s design, would be unable to read the contents of that environment.
All of this shows why the agent phase of AI will be far more complicated than the chatbot era. For a chatbot, the main question was: “Is the answer correct?” For an agent, the list is longer: “Did it understand the goal correctly? Was it authorized to take this action? Did it sufficiently check the consequences? And who is responsible if the result is wrong?”
Meta has an enormous advantage over many competitors: an existing network of products with billions of users. Facebook knows social relationships, Instagram knows interests, WhatsApp can become the interface for instructions, and smart glasses can become a sensor for the physical world. Muse could potentially bring those pieces together into one system.
But that same advantage is also the main source of risk. A personal agent requires far more trust in Meta than an Instagram feed or a conventional chatbot. A person is not merely allowing an algorithm to recommend content — they are admitting it into systems where real decisions are made.
Muse’s success therefore will not depend only on the quality of Muse Spark. It will depend on whether Meta can convince people that the agent is useful enough to take over part of their routine, while remaining predictable enough that users do not have to fear every automatically sent email, payment or reservation.
If that model catches on, the familiar internet could begin changing faster than it appears. People may increasingly stop opening a dozen services themselves, comparing options and filling out forms. They will state the desired outcome, while an agent visits websites, coordinates services and clicks the buttons for them.
Competition among technology companies would then move up another level. What matters will not only be whose model is smarter, but whose agent earns the right to become a person’s primary digital representative — the system that sees their intent first and decides through which services that intent will be carried out.
Muse, then, is not simply another Meta assistant. It is an attempt to occupy the space between the user and the rest of the digital world.
And if the chatbot era taught us how to talk to artificial intelligence, the agent era will ask a harder question: how much of our own lives are we willing to let it manage for us?
