Meet my Muse bot! Every morning, he dresses for the weather wherever I am. I think we could all use a little more whimsy in our lives. As it turns out, his raincoat isn’t even his best feature.
What is Meta Muse?
Muse is Meta’s personal AI agent. Meta describes it as “a secure, private personal AI agent that proactively helps with people’s goals and suggests ideas.” You can talk to it in the Muse app, over WhatsApp, or on the web at muse.ai.
Meta really nailed it with the launch of this guy. The user experience is delightful, and it’s really accessible in the sense that you don’t have to think about how or where to do something. You can simply describe what you need in the main chat and let Muse figure out which tools or context belong to the job.
A few tasks I gave it to start with:
Booking appointments: I needed to book a massage before an upcoming race. The user experience on that website is pretty cumbersome if you want to check availability across multiple days, so I handed the task over to Muse. He dug through weeks’ worth of options and came back with the best availability based on my schedule. Since the booking required a credit card deposit, he handed me a link to finalize the appointment myself.
Replenishing my hair products: I can order the products I use through a number of retailers, but I didn’t want to sift through who had the best price, who would ship the fastest (because I had, naturally, waited until my bottles were almost empty) and who offered the most cash back on Rakuten. Muse compared the options, found me a 10% Rakuten cash back offer, and sent me the link to check out. I’ll definitely reuse this workflow for any online shopping where I’m looking for a specific item.
Running my Return Tracker: The tracker reads my order emails, keeps a running record of return deadlines, and nudges me before a window closes. It saves me from having to remember which retailer gives me 14 days versus 30, or which clock started at shipping versus delivery. It also helps start the return for me and reminds me to drop off the package. I shared the full story of how I built it in my previous post, but it has become one of the clearest examples of what I actually want a personal agent to do.
The most useful proactivity I’ve seen so far: On September 19, I asked Muse to do a one-time scan of my inbox for subscriptions. That was permission for one pass, not a standing permission to keep reading my email. The scan surfaced that a free trial of one of my apps would renew on September 27, and Muse held onto that deadline. Then, on September 26, he warned me that the trial was about to turn into a $30 charge, without my having to remember to ask about it again.
Why this matters to me: the proactive part wasn’t knowing about the trial, it was deciding when it was worth bringing back up. He didn’t go rummaging through my inbox looking for new chores. He took something from a job I’d already given him and reminded me about it at the right moment. That’s the kind of proactive I want: attentive enough to catch what I’d forget, restrained enough not to invent errands or create unnecessarily noisy notifications.
Where it got blocked (for now)
Flight monitoring was one limitation for me. Muse can help research publicly available cash fares, but comparing mileage prices requires signed-in airline access, and United and American Airlines both blocked my Muse bot from signing in to my account, even when I took control of the Muse browser to handle the log in myself. I’m hopeful that more companies and retailers work to enable use cases like these for AI agents in the future!
On the plus side, Shopify announced a partnership with Muse! Muse will be able to shop on any Shopify merchant website and check out if you use Shop Pay.
Security seems thoughtfully designed
Meta seems to have put a lot of thought into the security side of Muse, which matters when you’re handing an AI access to systems like your email and calendar.
The way I think about Meta’s approach to Muse security: my Muse doesn’t work at some big shared desk. He has his own private office in the cloud with his own little computer and a door that locks. Everything he knows about me lives in that office, separate from everyone else’s Muse bots, and separate from Meta’s ad business. You can read more about the safety and security details in this Meta Help Center article and Meta’s overview on how they thought about safety
A few things I’ve noticed in practice:
Muse can’t see my passwords. Logging into things happens in a secure vault he has no visibility into.
Connecting my Gmail still required Muse to ask for my authorization on each new type of email task. For example, I had to authorize permission to search for specific types of emails related to online orders and returns for my Return Tracker. If I wanted to create a task to monitor subscription renewals, Muse would ask for my approval to search for those specific emails for that task. He doesn’t just go rifling through all my emails.
I’ve read that Muse won’t take other sensitive actions like making a purchase or sending an email without asking my approval first. I haven’t tested that use case yet.
Why I migrated my Return Tracker
I’ve migrated my Return Tracker to Muse for three main reasons:
My Scheduled Task would silently fail: This happened a couple of times when the task got blocked trying to start a return for me. ChatGPT tried to hand the task back to me, I forgot to respond, and that unfinished handoff ended up holding up the daily check for new return deadlines. I didn’t realize the task was actually paused for several days.
The way Muse set the tracker up by default was cleaner: the daily email scan for potential returns runs independently from any active return it is trying to start. If one retailer blocks the return process and Muse needs me to step in, that one return can wait for my action without stopping the monitor for everything else.
I personally found the user experience to be more intuitive in Muse: If I need to edit the task, change a rule, or ask about the status of an order, I can just drop the question into the main chat (or even any side chat) of Muse. Because Muse shares context and memory across chats, it can help without requiring me to find the exact conversation where the tracker was originally created.
The task structure seems more organized: When I went to edit the task instructions manually in ChatGPT, I noticed it was stuffing rules about individual past returns into the task instructions. This isn’t terrible in the near term, but over time the instructions will become increasingly bloated if the system has to reread a ton of notes about old order statuses every day it runs.
Muse set up the durable rules for how the tracker should work separately from the running record of specific orders. The instructions explain the job; the order ledger holds what happened. It is a small architectural difference that makes the system less error-prone over time.
To be fair, I may have been able to prompt the ChatGPT task into this type of structure. But that further highlights the point about Muse’s accessibility: With Muse, I didn’t have to specify the task architecture. I was able to give it a simple goal, and it separated the quiet daily scan for new return deadlines from the active return attempts by default.
One thing worth noting: While I’ve had better luck so far with Muse being able to successfully start returns on retailer portals, that may simply be luck. As I’ve mentioned earlier, many retailer sites are structured to block bot traffic. I’ve found that it’s pretty hit or miss as to whether AI can successfully navigate a retailer’s return portal on my behalf. I expect this to improve over time as more retailers build support for this type of workflow.
Where Muse fits in my stack
Since migrating my Return Tracker, I’ve been thinking more intentionally about where Muse fits into my life relative to other AI tools.
The key distinction I’ve made is to keep Muse dedicated to personal tasks. Anything related to work still stays in Claude or ChatGPT.
So far, I like that Muse has context and memory across my chats. Claude and ChatGPT offer Projects that create clearer boundaries around a specific body of work. Those project silos are extremely helpful for work tasks, where I may want one set of information and instructions kept neatly separate from another. But for a personal agent, I haven’t encountered the same need for strict separation.
My running training plan, travel preferences, calendar, shopping habits, and return deadlines all belong to the same life. I may organize them into different side chats so I can find things easily, but I don’t want to reintroduce myself or rebuild the relevant context every time I move between them. Muse’s shared memory makes those chats feel like different rooms in the same house rather than completely separate houses.
There are also tasks I’m still relying on ChatGPT and Claude for where built-up historical context really matters. For example, they have months’ worth of training data from my Garmin that gives them far more historical context about my running. I’d have to proactively migrate that data and context over to Muse in order to get the same level of analysis about my running progress.
What I’m evaluating next
I’m excited to see the Agent space evolve further. Full disclosure, I haven’t tested out Grok Bot yet. I wouldn’t be surprised if OpenAI or Anthropic launch their own agent competitor to Muse at some point soon. For now, Muse has earned a real place in my personal stack.
I’ll be trying out new agents as they arrive and look forward to seeing how each one differentiates itself!