On September 8 local time, Meta officially launched its personal AI assistant, Muse. Unlike traditional chatbots that answer questions and generate content, Meta positions Muse as a “personal AI Agent”: users simply state a goal, and Muse breaks down the task, opens web pages, fills out forms, and calls external applications to get the work done.
It can send and receive emails, schedule appointments, book travel, purchase goods, and even participate in bill negotiations on the user’s behalf. For tasks that cannot be completed immediately, Muse can keep running in the cloud even after the user closes the app, until the task changes or requires user approval. Mark Zuckerberg summarized it as a personal agent that “understands goals and works around the clock to complete tasks for users.”
What supports this continuous operation is the Muse Secure VM, a virtual computer in the cloud dedicated to each user. It has its own browser and can store user-authorized data and service connections, continuing to search for information, monitor status, or wait for the right execution moment after the user leaves.
Greater permissions bring greater risk. To address this, Meta has deployed an independent system called Sentinel within the virtual machine: Muse is responsible for understanding goals and proposing actions, while Sentinel reviews those actions and decides whether they can be executed directly, should be blocked, or must be handed to the user for approval. Sensitive operations such as sending emails or making payments will proactively ask the user; completed and pending actions are kept in an audit record; and for payments, Meta partners with Stripe to generate one-time card numbers.
This dual system design—executor plus supervisor—points to a governance problem that agents cannot avoid in real-world deployment: who is acting, what permissions have been granted, and how to trace issues when they arise. StarWar Technology has repeatedly emphasized the same point on its OPC agent collaboration platform: let agents work with autonomy, but also make the collaboration process manageable, controllable, and traceable, so multiple agents can coordinate within clear boundaries.
Muse did not appear suddenly. Meta formed a superintelligence lab in 2025 and spent about nine months rebuilding its AI technology stack. In April 2026, it released the Muse Spark model supporting multimodal reasoning and multi-agent collaboration, and around the same time internally launched a personal agent project codenamed Hatch. Internal testing led Meta to discover that for agents to work continuously, stronger models alone are not enough; they also need independent computing environments, persistent memory, application connectors, and background execution capabilities.
Meta’s vision for the business model goes beyond subscriptions. Zuckerberg said Muse will initially offer a free quota of about 100 million tokens per week, while over the long term the company hopes to generate revenue from payments, business services, and transaction commissions. Whether this can work ultimately depends on two things: whether it can reliably finish tasks over the long term, and whether users are willing to hand over enough data and permissions to it.