AI Agents and AI Agent Systems: What They Are, How They Work, and What You Can Use Them For?

AI Agents: What Are They, How Do They Work, and What Can You Use Them For?

More and more often, we hear about AI agents that can work for us, operate a computer, perform multistep tasks, and even make decisions independently. In short, AI agents work like the artificial intelligence we imagined in sci-fi movies. Such artificial intelligence can perform tasks (check information, edit multiple documents at once, shop, etc.). Few people imagined AI as a classic language model like ChatGPT.

Importantly, you don’t have to be a programmer or an artificial intelligence specialist to start using them. One of the most interesting applications of agents is ordinary work on your own computer: organizing documents, editing multiple files simultaneously, preparing spreadsheets, or creating simple programs. The next step is creating automated systems that are activated in various ways (e.g. by a message on WhatsApp) and that work “on their own in the background,” not only when we issue a command in ChatGPT.

Before we start calling every AI-powered system an agent, it is worth understanding a few distinctions. What exactly differentiates an agent from an ordinary chatbot? Is every automation using a language model an agent? Why do we use the form “Agenty” rather than “Agenci”? Greater AI autonomy means not only greater capabilities, but also greater risk—how can we protect against it?

In 2026, language model applications such as ChatGPT or Claude are to some extent AI agents. Especially in Deep Search mode.

A pure language model relies on predicting the next word based on its knowledge of the relationships between words. This is how the first public version of ChatGPT worked. However, it quickly became apparent that, in addition to improving the “language model engine” itself, the chain-of-thought mechanism and support for tools, such as internet access, produce very good results.

In such a case, instead of generating the response text immediately, the model assesses what the user needs, then writes a preliminary version, evaluates it, creates a web-search plan, analyzes the results, uses them to search for additional information, and then repeats the entire process. Currently, ChatGPT independently determines whether a given response requires an internet search and performs one, even if we do not explicitly ask it to.

However, the “agentic capabilities” of ordinary browser-based ChatGPT are still quite limited and are primarily used for generating text.

AI Agent on Your Computer, Working Directly with Files

True “agentic behavior” can be achieved in Work mode, or by installing ChatGPT or Claude on the computer’s drive. We need to have a paid version for this.

You surely have a mess of files on your drive. You want to organize them, standardize their formatting, correct errors, extract specific information from them, and prepare one consolidated summary.

With the traditional approach, you would have to open successive files, copy their contents into ChatGPT, issue commands, and then transfer the completed responses back into the documents.

However, this can be approached differently.

There are tools, such as OpenAI Codex or Claude Code, that allow you to work directly with files located on your computer.

You specify a folder and describe what you want to achieve. If the agent has the appropriate permissions, it can independently review its contents or open the necessary files, then perform operations and save the results. You do not need to manually provide each document to it separately.

Although tools such as Codex or Claude were created primarily with programming in mind, as you can see, they can also be used for many other tasks.

In addition to organizing files, the model can also edit multiple files at once.

Editing Texts and Excel Spreadsheets

For example, you can ask the agent to:

Reviewing 30 text documents, correcting typos, and standardizing formatting.
Analyzing a dozen or so Excel spreadsheets, combining the data, and preparing a consolidated summary.
Extracting names, dates, and amounts from a large number of documents and recording them in a table.
Changing the same information in multiple files simultaneously.
Comparing several versions of a document and preparing a single version incorporating the differences.

The most important thing is that the agent can make changes directly to files instead of merely displaying a response in the chat window.

A sample command could look like this:

> This folder contains 25 documents with product descriptions. Review all of them, correct linguistic errors, standardize the way parameters are recorded, and prepare an Excel spreadsheet containing product names, prices, and key features. Do not delete the original files.

Depending on the tool and its capabilities, the agent can use appropriate libraries, scripts, or programs to process documents. Of course, this does not mean it will always handle every format correctly, especially in the case of complex Word or Excel documents.

File Organization

The aforementioned one of the most practical applications, which is worth trying at the outset.

Most of us have folders containing hundreds of files with random names. Documents, photos, downloaded materials, reports, invoices, several versions of the same project.

The agent can analyze these files and suggest a way to organize them. For example, it can sort invoices by date, rename documents based on their contents, detect potential duplicates, or group materials by topic.

You can also commission it to prepare a catalog of all documents, together with a brief description of the contents of each file.

With hundreds of documents, the time savings can be truly substantial.

However, caution must be exercised. An agent that can move and modify files may also accidentally overwrite, misclassify, or delete something. Therefore, it is best to conduct initial experiments on test copies and, during actual work, require a preview of the planned changes.

Creating Your Own Programs Is Becoming Increasingly Easy

The second extremely interesting application of agents concerns programming. And, contrary to appearances, it is not aimed exclusively at programmers.

Until recently, creating even a simple application required at least a basic knowledge of a programming language. Today, in many cases, you can start by describing what exactly you need.

Let’s suppose you have a complex Excel spreadsheet and want to browse the information it contains more conveniently.

You can ask the agent to create a small application that loads data from a file, lets you filter, search, sort, and display it in charts. The table has too many columns, so it’s difficult to understand what’s there, and editing is more difficult? Within an hour, anyone can easily create a simple solution that displays a row vertically, or even across the entire screen, allowing you to move between rows using the arrow keys.

Or perhaps you need a calculator that converts specific values according to your rules. Instead of manually building complex formulas in a spreadsheet, you can commission the creation of a simple program with data-entry fields and a button that calculates the result.

What once required learning Excel formulas, and later the basics of programming, can now increasingly be done using natural language.

Of course, this does not mean that anyone can create reliable, sophisticated software without any knowledge. You still need to be able to assess whether the program does what we expect, verify the correctness of its calculations, and catch errors.

However, the entry threshold is changing.

Creating complex formulas in Excel is one of the better applications of vibecoding.

Creating a complex table with many formulas that calculate complicated things takes a lot of time and already requires more knowledge of spreadsheets. AI can generate the entire table for you. Excel or Google Sheets provide the right environment for this situation. Since the data and formulas are spread throughout the sheet in such cases, it is easy to understand how everything works. This is much easier than analyzing code.

Additionally, spreadsheets often allow the creation of macros, offering more possibilities than standard spreadsheet formulas. In this case, AI writes typical code, but it is in a relatively easy-to-understand language.

What Programs Are Better Not to Write Yourself If You Don’t Have Programming Knowledge?

General rule: code written for yourself, for your own use, or for a small, well-organized team is fairly safe. This applies to situations where you can explain how the system works to every user and are certain that no one has any reason to deliberately destroy it. By the same token, no one on the team has any reason to, for example, delete the entire Excel file that several people are working on. Of course, you should also be mindful of the program’s permissions, so that a bug cannot, for example, delete all your files. When it comes to the data in the program, it is a good idea to have import and export options, so that whatever you have done in the program is safe.

You also need to be careful with integrations. AI is a tremendous help with the tedious task of integrating things, but you already need to have some knowledge here; a common mistake is sharing API keys and other passwords.

A program that works without AI and as an AI add-on.

AI can write a program that operates completely independently, without internet access and without using any AI model tokens. However, it is also possible to create simple programs that use a language model as an “engine.” Claude is the most convenient option here, as it has features specifically designed for this purpose. Such a program can be made publicly available, and the people using it consume their own quotas. If we do not have a paid Claude plan, we can run the code directly in ChatGPT, but in that case the AI features are less seamless because they are activated using a regular command in the chat.

Programs made publicly available.

You need to be very careful here. In such a situation, users or customers scrutinize and test the program extensively, so any errors can harm its image. Additionally, the program must be properly secured.

Environments for sharing programs.

There are websites that allow you to create and share your own programs in a secure environment. This way, we avoid the risks involved in uploading your own code to a server. The simplest example here is Claude (although it is a highly limited solution).

Key isolation mechanisms include sandboxing, containers, or running code in a browser (including the user’s browser).

More advanced capabilities are offered by Lovable, Bolt.new, and Replit, which allow you to create, test, and publish applications without having to manage a server yourself. In turn, CodePen, JSFiddle, and StackBlitz enable code to be run and shared quickly, especially simple web applications. It is also worth mentioning Hugging Face Spaces, a platform intended, among other things, for publishing AI-powered applications.

They do not eliminate all threats, but they reduce the risk to your own infrastructure. However, they do not guarantee that the published application itself will be secure.

A good practice is to ask AI whether what we want to do requires special safeguards (although, of course, it should be remembered that AI can make mistakes).

What is it like to work with a programming agent?

For example, you ask:

Create a simple application in which I can upload an Excel file, select the columns I’m interested in, filter the results, and export the filtered data to a new file.

The agent can independently plan the program structure, create files, write code, run the application, test its operation, and fix any detected errors.

And if you’re not satisfied with the result, you can simply write what you’d like to change.

For example: “Add a search engine,” “Make the buttons larger,” “I want to be able to edit data directly in the table.”

The system can then find the relevant code snippets and make changes to the existing program.

This is precisely one of the greatest advantages of tools such as Codex and Claude Code: they are not limited to writing a piece of code but can work on an entire project comprising many interrelated files.

It can therefore be said that creating small, useful programs is beginning to resemble working with a spreadsheet. Not everyone will become a programmer, just as not every Excel user becomes a data analyst. But many more people will be able to independently create tools tailored to their needs.

What exactly is an AI agent, and how does it differ from a chatbot and automation?

To understand where the possibilities described come from, it is necessary to examine how systems using language models work.

AI agents can make decisions. But it’s not about a simple choice along the lines of: if X, do Y. An AI agent has a goal and looks for ways to achieve it. So you don’t have to provide it in advance with every option it can choose from. Instead, you give it a set of guidelines, objectives, and constraints.

From Language Model to AI Agent

Language Model and Chatbot

At the foundation of modern AI systems are large language models (LLMs). Put simply, they receive text and generate what comes next. Thanks to training on vast datasets, they can answer questions, write articles, analyze information, and create code.

However, the model itself does not need access to the internet, documents, or a computer. It can be run locally, without any additional tools. In that case, its primary task is processing text and generating responses.

Chatbot Equipped with Tools

Modern systems such as ChatGPT and Claude can do much more. They can search the internet, analyze documents, perform calculations, and create files.

For example, when you ask about a company’s financial results, the model can independently find the latest report, read the data, and prepare a summary.

Importantly, the model can decide for itself which tool it needs. This already resembles agentic behavior, although merely using tools does not yet mean that the system is an agent.

From Chatbot to AI Agent

The most important feature of an agent is its ability to independently select actions that lead to the achievement of a specific goal.

Let’s compare three commands:

In the third case, it is no longer about a single response, but rather a multi-step process in which the model selects subsequent actions based on the results obtained so far.

However, the boundary between a chatbot that uses tools and an agent is not clear-cut. The key factor is the degree of autonomy of the system.

Automation Existed Before AI Agents

Automation is not a new invention. Tools such as Zapier and Make have been enabling users to connect applications and perform specific tasks without human involvement for years. And in theory, practically every program is a form of automation.

For example, you can prepare a spreadsheet with posts intended for publication. The automation checks the dates, retrieves the content, and publishes it on selected social media platforms.

It can also save the addresses of published posts, send notifications, or report errors.

No artificial intelligence is needed for this.

Moreover, traditional automations also make decisions. They can use logical conditions, filters, branches, and loops. For example, if an email contains the word “invoice,” the system moves it to the appropriate folder.

The difference is that the procedures have been programmed in advance.

A Language Model Does Not Have to Be an Agent

Adding AI to automation does not automatically mean creating an agent.

Let's say you publish a post on Facebook and want to prepare a shorter version for X. Automation can pass the text to a language model, receive the shortened version, and publish it.

You can even add a second model that evaluates the quality of the text. If it considers the text insufficient, the first model prepares a revised version. The process repeats until an acceptable result is achieved.

This may still be ordinary automation, because the entire process flow was defined in advance. The models perform their assigned tasks, but do not independently decide on the next stages of the work.

When Does Automation Become an Agent?

Let’s imagine that instead of programming the next steps, you issue a general command:

“Prepare an article about the Polish housing market, including current data, city comparisons, and reliable sources.”

The agent may begin with a draft, then determine that it needs additional information, search the internet, compare data, perform calculations, and prepare charts.

If it discovers inconsistencies, it may change the plan and conduct an additional verification.

The key difference is that the model chooses the next actions itself, rather than merely following a precisely programmed scenario.

A typical agent workflow looks as follows:

However, this does not mean unlimited autonomy. An agent can operate within defined boundaries: using only selected tools, adhering to limits, and requiring approval for more important operations.

In practice, agents can also be combined with traditional automations. For example, Zapier triggers a process upon receiving a message, the agent decides how to handle it, and ready-made integrations perform the selected actions.

Such a solution combines the flexibility of AI with the predictability of classic automation.

Comparison: chatbot, automation, and agent

Solution	How does it work?	Example
Classic chatbot	Receives a message and generates a response	Writes the content of a post
Automation	Executes a process according to established rules	Publishes a post at a specified time
AI-powered automation	Uses a model for a specific step	Shortens the post and then publishes it
AI agent	Selects subsequent actions and tools independently within a defined goal	Analyzes the topic, searches for data, creates and verifies the material

This list should not be treated as a rigid technological division. Many modern systems combine several of these operating methods.

The key question is: does the model merely execute a specified step, or does it also choose which steps should be taken next?

Agents conducting research and analyzing information

One of the most natural applications of agents is searching for and analyzing information.

With a regular internet search, we receive a list of pages that we have to open, read, and compare ourselves.

A chatbot with internet access can do part of this work for us. Search for information, analyze the results, and prepare a response.

However, an advanced research agent can conduct searches across many successive stages.

Let’s assume we want to determine how the expenditures of five Polish cities on public transport have changed over the past ten years.

The agent can first search for budget reports, then identify the relevant items in the documents, extract the data, and prepare a table.

During the work, they may discover that some cities present expenditures according to a different classification. In that case, they should look for additional documents, identify the methodological differences, and indicate the limitations of the comparison.

If they do not find the necessary data, they can change the search method.

This is precisely an example of multistage decision-making.

However, autonomous research does not automatically mean reliable research. An agent may misinterpret a source, mix up data, or draw an unwarranted conclusion.

That is why, when conducting more serious analyses, it is particularly important to verify the sources, calculation methods, and key findings.

Computer-controlling agents: OpenClaw, Hermes, and other solutions

So far, we have focused mainly on agents performing specific tasks, such as document analysis, programming, or preparing reports.

There is, however, an even more ambitious direction: creating an AI assistant that can manage multiple tasks on a computer, use various programs, and also work when the user is not actively interacting with it.

Examples of such solutions include OpenClaw (a project previously known as Clawdbot and subsequently as Moltbot) and Hermes Agent.

These are systems that enable the integration of language models with tools, memory, messaging platforms, and command execution environments. Their capabilities depend on their configuration and the permissions granted.

If you have such an agent running on your computer or server, you can send it a message via Telegram and ask it to review the documents in a specified folder, check current information online, and prepare a report.

You can also set up recurring tasks, such as checking for new files daily, analyzing specific data, and sending a summary.

In some configurations, agents can also operate a web browser, execute system commands, and use other applications. This does not always mean literally clicking a mouse on the desktop. Often, the system performs operations through a terminal, API, or specially prepared tools.

OpenClaw focuses, among other things, on connecting agents with messaging platforms, organizing sessions, and executing scheduled tasks. Hermes Agent additionally offers mechanisms for maintaining memory and creating reusable skills.

In principle, such an assistant would have to be told from scratch each time what we do and what tasks we want to perform.

At the same time, it is important to distinguish the technical capabilities of such systems from their actual reliability. The more complex the environment, the more potential problems there are: commands being executed incorrectly, improper actions in applications, unexpected costs, or security threats.

Therefore, solutions enabling broad access to a computer require particular caution.

You can also achieve Hermes functionality using Codex or Claude Code.

Both solutions, in addition to managing files on your computer, allow you to connect your email, Google Drive, and other tools. With a little patience, they can be used as agent assistants.

Greater autonomy means greater risk

This is one of the most important aspects of the entire concept of AI agents.

In traditional automation, the process flow is usually relatively predictable. The programmer defines its actions in advance.

If we have prepared an automation that sends text to a language model, receives a shortened version, and publishes it to a specified account, we know at least what actions the system can perform.

Of course, it can still make mistakes. The model may prepare incorrect text, the automation may publish something inappropriate, and repeated queries may generate unnecessary costs.

So it is not the case that ordinary automations are inherently safe.

However, their scope of operation is usually more limited.

In the case of an agent, the situation is different.

Let’s suppose we give it access to a folder containing documents and instruct it to organize their contents.

The agent may decide for itself which files should be moved, which renamed, and which considered duplicates.

If it misinterprets documents, it can cause disruption on a much larger scale than a single failed chatbot response.

And if we also equip it with the ability to run programs, execute system commands, or access the internet, the range of potential problems becomes even greater.

The agent may, for example:

Overwrite or delete important files.
Make incorrect changes to multiple documents at once.
Run inappropriate commands in the operating system.
Send confidential information to an external service.
Perform costly or undesirable operations through connected applications.
Get stuck in an action loop, repeatedly using paid models and tools.

A particularly dangerous situation may arise when an agent reads content from untrusted sources, such as a website or a received message, and treats the instructions contained therein as commands to execute.

This is one of the problems known as prompt injection, that is, injecting instructions into the data processed by the model.

How to Start Using Agents Safely?

The most sensible approach is to start with small, well-defined tasks.

If you want to learn how to organize documents, prepare a test folder containing copies of a dozen or so files. Ask the agent to analyze them, propose a new structure, and present the planned changes. Make sure that the model does not have access to any other content.

Only later let him make these changes.

It is also worth following a few simple rules:

Limit access to files. If an agent is to organize one folder, it does not need permissions for the entire drive.
Work on copies and create backups. Especially before bulk renaming, moving, or deleting documents.
Require approval for important actions. Particularly for deleting files, sending messages, publishing content, or carrying out financial transactions.
Set limits. You can limit the operating time, the number of operations performed, or the costs of using models.
Check the results. Do not assume that technically correct execution also means substantive correctness.

For programming, version control systems such as Git are useful. They allow you to review changes and restore an earlier version of a project. For documents and other files, you should ensure that appropriate backups are made.

It is also worth using environment isolation mechanisms, i.e. so-called sandboxes, which limit what the agent can access. Such mechanisms are also present in development tools, including Codex. ChatGPT Learn +1

However, the most important rule is simple: do not give the agent more permissions than it actually needs to perform the task.

Is an agent always better than ordinary automation?

Definitely not.

Many tasks are much better left to traditional automations. If a system is supposed to retrieve data from one source every day at a specific time and save it in a table, we do not need an advanced agent.

A classic script or a process prepared in Make or Zapier can perform such a task faster, more cheaply, and more predictably.

Similarly, if the automation is only meant to pass text to a language model and then save its response, the additional layer of autonomy may provide no benefit.

An agent becomes particularly useful when it is not easy to predict all the necessary steps in advance.

This applies to tasks requiring the analysis of heterogeneous documents, information retrieval, solving unexpected problems, or working on projects in which subsequent actions depend on earlier results.

Very often, the best solution will combine both approaches.

Traditional automation can be responsible for launching a process, transferring data, saving results, and controlling permissions.

However, an agent can perform those parts of the work that require flexible analysis of the situation and selection of subsequent actions.

We can therefore use autonomy where it is beneficial, while retaining predictable mechanisms wherever they are sufficient.

Where to start?

If someone has so far mainly used standard ChatGPT or Claude, they do not need to immediately install a complicated system that manages the entire computer.

The easiest way to start is with tasks we already perform.

Preparing a report based on several documents, analyzing a spreadsheet, or compiling a summary from information found online makes it possible to understand how the model uses tools.

The next step could be to try Codex or Claude Code on your own computer.

Although they are primarily programming tools, they are also excellent for learning how to work with agents. You can point them to a test folder and ask them to analyze files, propose changes, or write a script to automate repetitive tasks.

What’s more, such work does not require creating a large project right away.

A good first task could be creating a simple calculator, an Excel data viewer, or a tool that automatically renames files according to a predefined pattern.

This makes it possible to see how the agent plans its work, which tools it uses, what mistakes it makes, and how it can be improved with further instructions.

Only once we learn to supervise such activities is it worth experimenting with more autonomous solutions that operate cyclically or handle multiple applications.

There is no need to hand over the entire computer to the agent right away. It is much more sensible to gradually increase the scope of its responsibilities.

AI Agents or AI Agenty? Which Form Is Correct?

Finally, there is still the issue of terminology.

In Polish, we encounter two forms: “agenci AI” and “agenty AI.”

The difference results from the declension of the noun “agent.”

“Agents” is a masculine-personal form, naturally referring to people, such as real estate agents, intelligence agents, or sales representatives.

“Agenty” is a non-masculine-personal form used in IT terminology, where an agent denotes a program or autonomous system performing specific tasks.

Therefore, in the context of software, the form “AI agents” helps emphasize that we are talking about computer systems, not people.

At the same time, the term “AI agents” is very widespread, including in technology and business materials. It makes no sense to treat it as unequivocally incorrect.

In this article, the form “agenty” appeared more frequently because it better reflects the technical nature of the solutions described.

The word “agent” is not, in fact, new to computer science. Agent systems and autonomous programs were studied long before the emergence of today’s large language models. The current popularity of the term is primarily due to the new possibilities created by combining language models with tools and action-execution environments.

Summary.

The most important change brought about by AI agents is not that artificial intelligence can write an even better text or provide a more elaborate answer.

More importantly, we can increasingly delegate the execution of an entire task to the system, rather than merely asking how it should be done.

We can ask it to analyze documents, organize files, prepare tables, create a program, or conduct multi-stage research.

The agent can independently choose subsequent actions, use available tools, and adjust its work based on the results received.

However, this does not mean that every system using a language model is an agent, nor that greater autonomy is always an advantage.

Conventional automations still make a great deal of sense. They are predictable, relatively easy to control, and often entirely sufficient. They can also be enhanced with language models without handing AI control over the entire process.

Agents are particularly effective when completing a task requires independently selecting steps, responding to unforeseen situations, and using various tools.

However, as their capabilities grow, so does the importance of security, limiting permissions, and overseeing the actions they perform.

Perhaps the most interesting thing is how dramatically the accessibility of technology is changing. Just as spreadsheets enabled millions of people to perform calculations without writing their own software, AI agents are beginning to enable people who have never programmed before to create simple programs and automate their work.

This does not mean that technical knowledge is no longer necessary. However, what changes is how much can be done without advanced knowledge of it.

And that is precisely why it is worth taking an interest in AI agents now. Not because they will soon do everything for us on their own, but because even today they can significantly simplify many ordinary, everyday tasks. agent ai illustration.webp

AI Agents and AI Agentic Systems. What are they, how do they work, and what can you use them for?