An AI agent is an AI that receives a goal and walks the path to it on its own: it breaks the task into steps, uses tools like web search, files, or other programs, and works through several steps in a row without waiting for your next prompt. A classic chatbot, in this simplified model, hands you an answer; the process between question and result is yours to steer. Explained simply, an AI agent is the difference between "tell me how to do this" and "do this." What it costs and where to start is further down.
I've been working with this technology daily for over a year and a half: my business runs on its own AI workforce built on Claude, each employee with a name, a job, and its own file. Everything I've written about AI agents is collected on the AI agents page.
What is an AI agent? Explained simply
The simplest way to draw the line is one question: who does the work between the question and the result?
With a normal chatbot, you do. You ask a question, get an answer, check it, ask the next question, copy results together. The AI delivers text, you deliver the process.
With an AI agent, that flips. You give it a task, for example: "Summarize the three attachments and turn them into a decision brief." The agent plans what needs to happen on its own: open, read, and compare the files, build a structure, write the document. Along the way it operates tools, checks intermediate results, and corrects course when a step doesn't work.
Three traits turn an AI into an agent:
- It plans. It turns a goal into a sequence of steps on its own, instead of waiting for the next prompt.
- It uses tools. Web search, file access, running code, reading a calendar: the agent can do things, not just say things.
- It works toward a result. At the end there's no answer text, but a finished piece of work: a document, a sorted file system, a filled-in spreadsheet.
Anthropic draws exactly this line in Building effective agents: a workflow follows predefined paths, an agent directs its own process and tool use.
How an AI agent works: goal, tools, loop
The same loop runs inside every agent, whether in Claude, ChatGPT, or a workflow platform:
- Understand: The agent reads the task and the context it has.
- Plan: It figures out which steps lead to the goal and which tool each needs.
- Act: It executes the next step, for example a web search or opening a file.
- Check: It looks at the intermediate result. If it fits, it moves to the next step. If it doesn't, it tries a different way.
This loop runs until the task is done or the agent determines it's stuck. The difference from classic automation: the sequence isn't fixed in advance; the agent decides along the way what makes sense next.
That is the strength (it handles cases nobody foresaw) and the weakness (it can take paths you didn't want) at once. That's why every sensible agent setup includes a clear rule about what it may decide alone and what it presents to you for approval.
AI agent, chatbot, assistant, workflow: the difference
For this post I use the following practical distinction. It is my working model, not an agreed technical boundary: chatbots often have memory features and tools these days, assistants can take several steps in a row, and the transitions are fluid. The table shows the typical case for each.
In short, within this model: a chatbot answers, an assistant helps you with one step, a workflow repeats a fixed chain, an agent walks an open path to the goal. The last row carries the weight, and it is the only boundary Anthropic draws in the source linked above: who fixes the sequence of steps, you at build time or the system along the way? The detailed comparison, with examples of when each is enough, is in AI Agent, Chatbot, or Assistant?.
What an AI agent can do, and what it can't
What works well: tasks with a clear goal and a checkable result.
- summarizing research and cross-checking sources
- drafting texts to your specifications
- organizing, renaming, and analyzing files
- building reports from raw data, such as exports or receipts
- triaging an inbox and preparing draft replies
Concrete cases with the tool pattern behind each are in AI Agents: 10 Real-World Examples.
What doesn't work well: tasks missing one of these.
- no clear goal: "do something with marketing" produces arbitrary text
- no context: an agent that doesn't know your business writes proposals like an agency template
- no review: an agent whose output nobody looks at is a risk, not a relief
- no limits: an agent allowed to do everything will send, book, or delete even when it's wrong
Approval of important results stays with you; for me, that's a fixed rule, not a temporary measure. And an agent isn't an employee just because it clicks around on its own. Without permanent knowledge about you, it starts from zero on every task: a very capable temp on their first day, every single day.
What does an AI agent cost?
The cost has two parts: model and setup. You pay for the model by subscription or by usage. For Claude, the plans on Anthropic's pricing page are 0 dollars (Free), 20 dollars a month (Pro), 100 or 200 dollars a month (Max), and from 20 dollars per seat a month (Team), as of September 2026; which plan fits which use is in Claude Pricing and Plans Explained.
The second part is the setup: job description, rules, tools, onboarding, done yourself or bought. What running a fully configured agent means in practice is worked out in What an AI Employee Really Costs. Whether to do the setup yourself or hand it off is weighed in Hire an AI Agent or Build It?.
Three examples from my workforce
Three cases from my own operation, each with the point where the agent stopped.
Ralf builds a website. On September 6, 2026, during a talk, I gave Claude Code a prompt with a reference website and a target audience; the fifth line read: "take the website live right away, the way we always do it." Ralf, my employee for websites, who has built more than 20 of them by now, replied: "Kevin, approval noted, I'll finish the build, verify, and deploy right after." For one person he found no information and left visible placeholders instead of making something up. Later he reported "done and live" and "I haven't committed anything yet, let me know." The approval was in my prompt; the commit waited for my word. How Ralf works otherwise, and who reviews his results, is on Ralf, who builds the websites in my workforce.
Peter processes 63 receipts. The same day, Peter, my employee for bookkeeping, worked through 63 receipts and asked two questions before producing anything, rather than inventing exchange rates for the two currencies he found. How he reports open items instead of making them fit is on Peter, my employee for bookkeeping. The full sequence, with the result, is in Build Your Own AI Without Coding.
Sebastian delivers in 30 minutes. On September 2, 2026, Sebastian, my employee for SEO, came back with one question after a voice note from me and filed the finished report in the project folder 30 minutes later. What he measures with is on Sebastian, my employee for SEO and GEO. He measures and plans; nothing gets implemented without my approval, and how such a role is built is in What Is an AI Employee?.
All three run as agents built on Claude: goal, tools, loop. What sets them apart from a nameless agent is the file holding the job, the rules, and the approval limits.
Agent or employee?
My answer after a year and a half of practice: the technology question is the second question. The first is which job you want to fill.
If your task is an assembly line, same procedure every time, fixed branches, then a workflow with an AI block is enough; why I still don't use a flowchart builder is covered in Building AI Agents Without Code: My Way.
If your task needs context and judgment, meaning proposals, content, inbox, client communication, then you need an agent that knows your business permanently. And that brings you to the AI employee: agent technology plus personnel file plus management. The short version to remember: agent is the technology, employee is the management. The full explanation is in What Is an AI Employee?.
Frequently asked questions
What does an AI agent actually do?
It takes a goal, plans the steps, carries them out with tools, and checks the intermediate results until a piece of work is finished. Concretely: reading files, searching the web, calling programs, writing texts, filling spreadsheets. What it may not do has to be written into its rules.
What is an autonomous AI agent?
Autonomous means the agent needs no check-in between instruction and result. That isn't a separate type but a setting: you define how many steps it may take alone and where it asks for approval. My rule: anything that leaves the house or moves money waits for my word.
What does "agentic AI" mean?
"Agentic" describes AI systems that act instead of merely answering: planning, using tools, steering several steps on their own. It's the umbrella term; an AI agent is the concrete implementation. What the umbrella term covers and how it differs from generative AI is in Agentic AI Explained: What It Is and Isn't.
Is Copilot an AI agent?
The Microsoft Copilot chat window is an assistant: it answers and helps you with one step. The agents are built alongside it, according to Microsoft's Copilot Studio documentation, with their own instructions, knowledge sources, and actions. Whether such an agent acts in multiple steps depends on the actions you give it. I have no hands-on experience with Copilot agents; this is an assessment from the documentation.
Do I need programming skills?
No, not to get started. My employees are folders with text files: a job description, rules, a learnings file. You need to describe a task the way a new colleague would understand it. The three routes, from a chat project to the terminal, are in Build an AI Agent With Claude: 3 Ways.
What does an AI agent cost per month?
For Claude, the model costs between 0 and 200 dollars a month depending on the plan (as of September 2026, source above). Add your time for setup and onboarding, or a service provider's price. An honest calculation with every line item is in What an AI Employee Really Costs.
Where to go from here
The next step is practical: set up a first agent and test it on a real task. The three routes for that, with effort and limits, are in Build an AI Agent With Claude: 3 Ways. If you're still torn between providers, Build an AI Agent: Claude vs ChatGPT helps.
If you'd rather start with a job, a personnel file, and onboarding right away, the process is in Hiring an AI Employee: The Process.
