Using AI as an Employee for Weeks: A Small Business Owner's Real Record

Most people use AI by asking it questions, but some have chosen a different approach: letting it do the work. A small business owner who runs a media outlet, a tax firm, and a nonprofit organization used Meta's AI assistant Muse as a genuine team member for several weeks and documented the experience.

What Muse Actually Is: Not a Chat Window, but an Execution Agent

The fundamental difference between Muse and an ordinary chatbot lies in its positioning. An ordinary chatbot is a passive information provider—you ask, it answers, it tells you what to do. Muse is an agent: it can browse the web, fill out forms, create documents, edit files, connect to accounts like Gmail or Facebook after user authorization, and execute complete multi-step tasks.

The user gives instructions in natural language—this business owner even frequently used Spanish voice dictation—and Muse presents results after execution, waiting for user approval at critical steps. In the author's own words: "It doesn't tell you how to do it, it just does it."

This shift from "information assistance" to "task execution" is the core marker of the leap in AI tool capabilities.

AI Agent is an important evolutionary direction relative to traditional generative AI. An ordinary large language model (such as ChatGPT's basic conversation mode) is essentially a "text-to-text" mapping function: input a prompt, output a response, with capabilities bounded at the information level. An AI agent, however, layers three major capability tiers on top of the language model: tool use, planning, and memory. It can break down a vague goal into several sub-steps, select appropriate tools at each step (browser, email client, file system, API interfaces), dynamically adjust the next step based on execution results, and request human confirmation when necessary. The article's mention of "Muse connecting to Gmail or Facebook accounts" relies on the OAuth authorization mechanism—after user authorization, the agent receives a limited, revocable access token rather than directly obtaining account passwords, which is the standard security design practice for agent-type products. Currently, mainstream agent products also include OpenAI's Operator and Anthropic's Computer Use, among others.

What It Actually Did: Real Tasks Across Four Business Scenarios

The author comprehensively tested Muse's capabilities across multiple businesses, covering a remarkably wide range of scenarios:

In SEO optimization, Muse helped optimize titles, descriptions, and keywords for hundreds of articles, processing them in batches based on Google Search Console data. It also diagnosed website speed issues and performed technical fixes that previously required hiring a developer.

In e-commerce setup, at the author's tax and notary firm, Muse helped build a complete online store—ten digital products (immigration guides and practice tests) with credit card and PayPal payment. It then completed preparations for an Etsy shop, as well as planning a WordPress maintenance service package.

In website rebuilding, it redesigned the entire website for the nonprofit organization, making it look more professional.

In daily operations, client bookkeeping, PDF receipts, construction estimates, flyers, news images and their Facebook posting (text always reviewed by the author), trademark registration, and IRS certified agent applications—all these long-process matters were gradually advanced.

The pattern the author describes is highly consistent: "I give the command, Muse executes and verifies, and I approve with a single 'yes.' Work that used to take days or require hiring several people now moves forward within hours."

The Google Search Console (GSC) mentioned in the article is a free tool Google provides to website owners for viewing their site's performance data in Google search results, including how many impressions each article receives, click-through rates, average rankings, and which keywords drive traffic. The typical SEO optimization workflow is precisely based on GSC data: identify pages with "high impressions, low clicks," work backward to determine whether their titles and descriptions are insufficiently compelling, then rewrite them accordingly. This also explains why Muse needs to "process in batches"—GSC has row limits on data exports, so hundreds of articles require multiple rounds of retrieval and rewriting. Another reference, IRS PTIN, refers to the IRS's "Preparer Tax Identification Number," which anyone who is paid to prepare tax returns for others must hold, and both application and renewal must be completed through the IRS's official system. Government system maintenance windows and verification mechanisms are beyond third-party control, which is also the objective reason why AI agents most easily "get stuck" on such tasks.

The Other Side Nobody Tells You About: Supervision Costs Still Exist

The author candidly shared the less glamorous parts. AI agents are not perfect employees: pages occasionally get stuck, government systems are sometimes under maintenance (the IRS PTIN system was once inaccessible all night), and data errors need correction.

The principle the author follows is simple: Muse proposes and executes, but final decisions and approval authority rest with the human. Any content must be confirmed by the author before being sent, published, or paid for.

Security boundaries are equally clear: passwords and sensitive data are entered by the author personally on official websites. Certain steps—such as Etsy's selfie identity verification—cannot be completed by any AI on your behalf.

This experience reveals an important reality: AI agents can indeed dramatically compress execution time, but the "human-in-the-loop" supervision model remains a necessary component for now. What is saved is execution time, not judgment or responsibility.

Human-in-the-Loop (HITL) is a key design principle in AI agent deployment, referring to the mandatory introduction of human confirmation at critical nodes in automated workflows, rather than letting it run end-to-end unattended. The fundamental reason HITL is necessary is that current AI agent failure modes are unpredictable: models may confidently execute incorrect operations even when uncertain, and may also be manipulated by malicious instructions embedded in web pages (so-called "prompt injection" attacks). For scenarios involving fund payments, public publishing, and legal filings, the cost of a single erroneous execution far exceeds the time cost of human review. The industry's general experience is: the more capable the agent and the broader its authorization scope, the more necessary it is to set hard confirmation points before "irreversible operations." The article's "passwords entered personally, confirmation required before payment and publishing" is precisely a straightforward implementation of this principle, and it also illustrates that the reasonable positioning of AI agents at this stage is "efficient executors" rather than "trustworthy decision-makers."

Not Replacing Employees, but Amplifying the Boss

The author's conclusion after weeks of use is worth pondering: this technology is not here to replace small business owners, but to amplify them. One person, plus judgment and a good AI agent, can now drive the workload that previously required a small team.

The key capability here is not the technology itself, but the ability to "harness tools"—learning to give clear instructions and learning to review results. Those who master this capability will gain a substantial advantage over those who ignore it.

The author has already made his choice: Muse is part of his team. And by his assessment, this is only just beginning.

For independent developers, small business owners, and freelancers, this experience provides a concrete reference: the value of AI agents is not in showing off technical prowess, but in turning "one person doing a team's work" from a slogan into an operable daily reality.

One-line Summary

A small business owner used AI agent Muse for cross-business tasks, concluding after weeks of testing: it amplifies rather than replaces the boss.

Paragraph Summary

A small business owner who simultaneously runs a media outlet, a tax firm, and a nonprofit organization used Meta's AI assistant Muse as a team member for several weeks. Unlike passive Q&A chatbots, Muse is an execution-oriented agent that can browse the web, fill out forms, edit files, and execute multi-step tasks. It broadly took over execution work in SEO optimization, online store building, website rebuilding, and daily operations, compressing workloads that previously required days or even multiple people into hours. But the author also candidly admits that supervision costs still exist: pages get stuck, government systems undergo maintenance, data errors occur, critical steps require human approval, and sensitive information like passwords is entered personally. The core conclusion is that AI amplifies rather than replaces small business owners—what is truly scarce is the ability to give clear instructions and review results.