Oct 15, 2026 · 55 min · 11 segments
What happens when AI stops talking and starts doing? Mikah Sargent puts Meta's Muse agent to work on simple jobs, hard ones, and a couple of weird ones: a weekly news brief, a day trip, a shopping…
Mikah SargentHost
Last week, uh, and by the way, thank you to everyone who sent in your feedback.
And if you remember that episode, you know that when I say piece of text, I'm being very literal because it wasn't just the next letter, it was the next token, and sometimes a token can be multiple letters.
Well, the model that we're working with today, which is Muse from Meta, is a model that still generates tokens just like last week, don't get it confused, but what's new is that software can now carry out the tool instructions that it generates.
So on top of being a model that has at its core, at its heart, the ability to generate new tokens based on what you input, it can also, in a way, communicate with programs, and that is what makes it all the more magical because an agent will choose steps, it will use tools, and then it will check what happened as it works toward whatever goal you set it after.
So if we look at it in comparison to a chatbot, a chatbot tells you how to book the table, but an agent books the table for you.
When it comes to an agent, it's all about the agentic loop, and frankly, it boils down to four steps if you can believe it.
Because when you give an agent a request, there are four things that it needs to do.
It will start by figuring out what your request is by reading or ingesting your request and then making a plan from it.
Then what does it need to do? Well, it looks at what tools it has available to it, like opening a website, searching the website, writing a file, and then it writes an instruction for that tool.
So it's all little software programs communicating with other little software programs.
It will look at what actually came back, and then, if you can believe it, it repeats the process again, and again, and again, and again until the job is done or it needs you to make a decision on its behalf.
And that last part is the part that matters because not every agent is going to do that, but a good agent knows when to stop and ask.
And a good agent is often created by a person who gives the right kinds of instructions.
Now, it lives in its own little cloud computer, and what that means is that everybody who signs up for a Muse account gets a dedicated Muse agent that has its own computer.
Last week, uh, and by the way, thank you to everyone who sent in your feedback.
And if you remember that episode, you know that when I say piece of text, I'm being very literal because it wasn't just the next letter, it was the next token, and sometimes a token can be multiple letters.
Well, the model that we're working with today, which is Muse from Meta, is a model that still generates tokens just like last week, don't get it confused, but what's new is that software can now carry out the tool instructions that it generates.
So on top of being a model that has at its core, at its heart, the ability to generate new tokens based on what you input, it can also, in a way, communicate with programs, and that is what makes it all the more magical because an agent will choose steps, it will use tools, and then it will check what happened as it works toward whatever goal you set it after.
So if we look at it in comparison to a chatbot, a chatbot tells you how to book the table, but an agent books the table for you.
When it comes to an agent, it's all about the agentic loop, and frankly, it boils down to four steps if you can believe it.
Because when you give an agent a request, there are four things that it needs to do.
It will start by figuring out what your request is by reading or ingesting your request and then making a plan from it.
Then what does it need to do? Well, it looks at what tools it has available to it, like opening a website, searching the website, writing a file, and then it writes an instruction for that tool.
So it's all little software programs communicating with other little software programs.
It will look at what actually came back, and then, if you can believe it, it repeats the process again, and again, and again, and again until the job is done or it needs you to make a decision on its behalf.
And that last part is the part that matters because not every agent is going to do that, but a good agent knows when to stop and ask.
And a good agent is often created by a person who gives the right kinds of instructions.
Now, it lives in its own little cloud computer, and what that means is that everybody who signs up for a Muse account gets a dedicated Muse agent that has its own computer.
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