Muse: a personal AI agent for continuing tasks

Meta launched Muse on 8 September as a personal AI agent that can keep working after a conversation ends. For people managing scattered plans, email and small projects, the useful question is how much follow-through it can take on while leaving decisions visible and controllable.

A continuing task needs more than a chat reply

Meta describes Muse as running on a dedicated cloud computer with a browser and connections to other apps. Its design article shows goals, activity history and files that continue beyond a single response. The homepage below shows the current public entry point, rather than an authenticated workspace or evidence of our own testing.

Muse’s public homepage, captured by WhatAI on 10 September 2026.

The useful change is follow-through

In its design article, Meta describes an assistant that works with goals and produces documents, PDFs, webpages and small interactive tools. Its examples include organising family plans and turning a learning goal into a useful guide. These are vendor examples. They indicate the intended scope, but do not establish how reliably a particular task will run in your account.

For a creative project, a sensible first experiment is to give Muse a bounded research job: compare three options against a budget and deliver a short document with sources and unresolved questions. A usable brief is easier to evaluate than an open-ended instruction to manage the whole project. If that works, a continuing goal could track decisions and follow-up work as the project develops.

Control is part of the workflow

The official tour shows an activity view and approval controls alongside Chat, Feed, Ideas, Goals and Library. That matters because work performed in the background can otherwise become difficult to inspect. Before connecting email or a calendar, decide what the assistant may read, what it may prepare and what still needs your confirmation. Check the actual permissions shown in your account.

Meta’s 8 September launch and safety article says permissions are enforced outside the agent itself. It also acknowledges that mistakes and prompt injection remain concerns. The promised confidential-computing protections are planned for later in 2026 and should not be treated as a current general guarantee. Review data and training settings before sharing sensitive material.

Test one useful outcome before expanding access

Begin with research that does not require a purchase or an outbound message. Ask for the evidence, inspect the resulting document and compare the activity history with your instructions. For bookings, purchases or communication, make the approval boundary explicit and confirm the proposed action before it happens. An attractive demonstration is not evidence that every unattended task will finish correctly.

Muse’s public site provides a sign-in route, but the material reviewed here does not settle pricing, regional eligibility or access for every account. Verify those details before planning a regular workflow around it.

WhatAI’s assessment is that continuing work and reusable outputs make Muse worth investigating for personal administration and project support. The meaningful result is a reliable, inspectable deliverable with less follow-up from you. If you try it, share the task, the output and where you needed to intervene in the tool’s community discussion. That is more useful than a general verdict about how clever it feels.