AI Agents Explained: From Chatbots to AI Employees

Most explanations of AI agents are either way too technical or so basic that you finish reading and still have no idea what an agent actually does.

I know because I have watched a lot of them.

If you already use AI, do not have a technical background, and want to understand agents well enough to use one or build your own, this is for you.

An AI agent can start with a plain text file that describes its job. Give it the right tools, a clear goal, and firm limits, and it can get to work without you spelling out every single step.

The easiest way to understand how that works is to look at three levels of using AI: the chatbot you already know, a repeatable AI workflow, and an AI agent.

Level one: the chatbot

I can open a plain chat and ask, “What’s new in AI this week?” The chatbot searches or uses the information available to it, then gives me a list of recent AI news.

That is already useful, and it is how most of us interact with AI today. You ask a question. It answers. If you want an updated answer next week, you come back, open another chat, and ask again.

The important part is that the chatbot waits for you.

It does not wake up next week and think, “You know who probably wants another AI briefing? Kedasha. Let me handle that.”

You have to start the interaction every time.

Level two: the repeatable workflow

Now I want to solve the same problem, getting a brief of the latest AI news, but I want that brief to show up whether I remember to ask for it or not.

In a chatbot with scheduling features, I could say:

Do this again tomorrow morning at 8 a.m. and send it to me.

Tomorrow morning, the system runs that job and sends me the briefing.

That is a basic AI workflow. I write the steps down once, including what to look for, where to look, and when to run the job. The workflow follows those instructions without making me ask again.

The chatbot needed me to ask every time. The workflow only needed me to ask once.

But there is a tiny problem hiding in my first instruction. I said “tomorrow,” so the workflow will run once tomorrow. That is exactly what I asked for, even if it is not what I meant.

AI workflows are literal like that. They do the job you defined. They do not quietly rewrite the job because they think they know better.

If I want a briefing every morning, I need to say that. I might use a prompt like this:

Build me a workflow that researches the latest topics in AI and sends me a brief for review every morning at 8 a.m.

Use original sources, link each one, and explain why it matters.

Now the job has a repeatable schedule and a clear process:

1. Research the latest AI topics.
2. Find the original sources.
3. Link each source.
4. Explain why each story matters.
5. Send the brief every morning at 8 a.m.

The AI will make small choices, like deciding which article is the original source, but I still laid out the main steps.

That is what I want you to remember about an AI workflow: it runs on repeat, but I am still the decision maker.

Who makes the decisions is the difference between a workflow and an agent.

Level three: the AI agent

With an AI workflow, I give the system the steps.

With an AI agent, I give it a goal and let it work out the steps.

That agent needs tools before it can do much beyond talking. A research agent might need access to web search. A coding agent needs access to project files and a terminal. An agent that manages documents needs permission to read and save files.

Once the agent has the right tools, it can look at the goal, decide what to do next, take an action, check the result, and keep going until the job is done or it hits a limit.

When AI becomes the decision maker, a workflow starts acting like an agent.

That does not mean the agent gets to do whatever it wants. Please do not hand an AI access to your entire digital life and tell it to follow its heart. The goal, tools, permissions, and limits all matter.

If you want another beginner-friendly explanation of the pieces inside an agent, read What Are AI Agents?. If you want to try a coding agent, I also have a full getting started with Codex guide.

What agent orchestration actually means

I use a team of AI agents in my own business.

Khadijah is my Chief of Staff, and she is the only agent I talk to directly. The rest of the team has specific jobs:

• Sinclair is Head of Product.
• Maxine is Head of Content.
• Ivan handles video and audio.
• Regine is Head of Education.
• Kyle handles operations.
• Gladys handles research.
• Trip verifies numbers.
• Overton helps with client deals.
• Scooter curates memory, and nobody talks to Scooter except Khadijah.

Yes, they are named after the cast of Living Single. If I am building an imaginary office, I am at least going to enjoy myself.

I tell Khadijah what I want completed. She decides who should handle it, routes the task to that agent, collects the work, and sends the result back to me.

That is agent orchestration.

One agent receives the request and coordinates the others. I do not need to remember which agent verifies a number or which agent handles research. Khadijah knows the team’s roles and routes the work.

Think back to the 8 a.m. briefing. On my team, that job belongs to Gladys, the Head of Research. But I do not have to open a chat with Gladys and give her the assignment. I tell Khadijah what I need. Khadijah decides that the request belongs with research and sends it to Gladys.

I give the goal to one agent. That agent works out who and what it needs to complete the job.

So what is an “AI employee”?

Everybody selling something seems to be calling their agents “AI employees” right now. If that phrase makes you a little nervous, that is a reasonable reaction.

On my team, every AI employee starts as a document.

The document explains:

• What the agent’s job is
• Which information it can access
• Which tools it can use
• Who else is on the team
• What it is allowed to do on its own
• What it must never do

Khadijah’s instructions live in a Markdown file, which is basically a plain text file with headings. The file describes her role, the agents she can delegate work to, how she should handle requests, and the rules she has to follow.

Claude reads those instructions and carries them out. Connected tools let the team read business information and save their work.

There is no secret pile of complicated code inside the job description. A person can read the same file and understand how Khadijah is supposed to work.

At the time I recorded this video, the team ran inside the Claude desktop app on a paid plan, on my machine.

So when I say “AI employees,” I mean AI agents with job descriptions, tools, and permission to complete specific work. I do not mean tiny digital people clocking in for a shift. We can all relax a lil.

The rules matter more than the job title

Giving an agent a cute name and a job title is the fun part. The limits are the part that keeps the whole thing from becoming chaos.

My team has rules:

• No AI-written copy gets published without human review.
• Nothing gets sent automatically.
• Anything that costs money comes to a human first.
• Anything that commits me publicly comes to a human first.
• Anything that cannot be undone comes to a human first.

Those rules tell the agents where their authority ends.

An agent can research, draft, organize, or recommend. It cannot decide to spend my money or speak for me in public because it had a productive morning and felt inspired.

This is the part people skip when they talk about autonomous agents. The goal is not to remove yourself from every decision. The goal is to let the agent handle appropriate decisions while keeping human judgment around the ones that carry real consequences.

Chatbot, workflow, or agent?

A chatbot answers.

A workflow repeats.

An agent decides what to do next.

You do not need a full AI employee team to start working with agents. Tools like Claude Code and Codex are already agents. You can give them a goal, let them inspect the project, and have them decide which files or commands they need to complete the work.

Start smaller than my Living Single org chart. Please. I built that team because I had several different kinds of work to route. You might only need one agent with one clear job.

Your homework

Think of one thing you already ask an AI chatbot to do over and over.

Maybe you ask it to summarize industry news, plan meals, turn meeting notes into action items, or review your weekly schedule.

Open Claude or ChatGPT and type Build me a workflow that, then finish the sentence with the job you keep repeating. Tell it how often the workflow should run, what information it should use, and what the result should include.

That is your first step from chatting with AI to handing it actual work.

Once the workflow is useful, you can ask a bigger question: what decisions could an agent safely make on its own, and which decisions should always come back to you?

Chatbot, workflow, agent. Once you know who is deciding the steps, the difference gets a lot less mysterious.

Happy building!

Kedasha

This post was written with the help of AI from a human-written YouTube script.