In this article, you will learn what an AI agent is, how it works, and how it differs from a classic AI chatbot. We explain why an AI agent is ideal for automating business processes in the background, how it can independently execute tasks, work with data and tools, and where in the company it can save the most time. We look at the difference between an AI agent and an AI chatbot, show typical use cases, and explain how an AI agent fits into the strategy of a modern digital company. The article is intended for companies in the world that are considering deploying an AI agent for process automation, customer support, back office, or internal workflows and want to understand how an AI agent works in practice.
An AI agent is a tool for smart automation that quietly works in the background and handles tasks without human intervention. In this article, we look at what an AI agent is, how it works in practice, and why it is starting to become as important for companies as an AI chatbot. You will also learn in which processes deploying an AI agent makes the most sense.
An AI agent is a digital “worker” that can independently perform tasks using artificial intelligence. Unlike a classic AI chatbot that only answers questions, an AI agent is designed to understand a goal, plan steps, call different tools, and gradually carry the task through to completion.
An AI agent is therefore not just a smarter chatbot, but rather a virtual assistant that can make decisions based on data and context. Thanks to large language models, an AI agent can work with text, documents, databases, or company systems and connect them into one smooth workflow.
Companies today use AI agents to automate repetitive processes, customer support, work with orders, document generation, or internal assistance for employees. When we ask “what is an AI agent”, we are talking about a new generation of automation that combines artificial intelligence, integrations with systems, and smart decision-making in real time.
An AI chatbot is a visible conversational assistant that the user communicates with directly, whereas an AI agent runs in the background and automates processes without direct human interaction. An AI chatbot typically appears as a widget on a website or in an app, answers questions, and can trigger actions in systems, such as changing an order, creating a ticket, or finding a document.
An AI agent, on the other hand, acts as an invisible digital colleague that does not handle the conversation itself but the logic and automation behind it. It often waits for an event, such as a new order being created, an email arriving, or a form being submitted, and based on predefined rules or using AI decides which steps to take. It can call APIs, work with databases, coordinate multiple tools, and create complex workflows.
So when you are deciding whether to choose an AI chatbot or an AI agent, it mainly depends on whether you need smart communication with the customer, or quiet but powerful process automation in the background. In practice, an AI chatbot on the frontend and an AI agent backstage are often combined, working together to deliver a complete experience for both the customer and the company. Do you want to understand the difference between an AI chatbot and an AI agent in detail? Take a look at our website where we break it down in depth:
An AI agent works on the principle of goals and steps. First, it receives an assignment, for example: “Find out why the customer has a problem with their order and prepare a draft reply.” The language model processes the input, the AI agent plans the steps it needs to take, and then gradually calls tools, APIs, or databases.
It can connect to a CRM, e-shop, helpdesk, or internal documents, retrieve information, evaluate it, and decide what the next logical step is. An AI agent often works in a cycle of perception, decision, and action. That means it reads data, evaluates the situation, takes a step, looks at the result again, and continues until it reaches the goal.
Unlike a simple AI chatbot, an AI agent is therefore not just a passive answer machine but an active unit that proposes solutions itself. In practice, an AI agent can not only answer the customer but also directly change the order, create a ticket, or update a record in a company system.
An AI agent is not just one universal type. In practice, there are several types of AI agents depending on what problem they solve:
In more complex systems, whole teams of AI agents can cooperate, each with a different role, similar to people in a team. One AI agent can be an analyst, another a coordinator, and a third an executor of specific actions in systems.
For companies, it is crucial to choose the right type of AI agent depending on whether they need help in customer support, back office automation, or, for example, internal training.
An AI agent is used wherever repeated work with information, rule-based decision-making, and communication with people or systems is needed. Typical AI agent use cases include:
In practice, an AI agent often looks very inconspicuous. It can be integrated as a widget on a website, as an internal panel in a CRM, or as a standalone application for employees.
A company can, for example, deploy an AI agent in customer support. It receives queries via chat, e-mail, or forms, automatically sorts them, resolves the simpler cases on its own, and passes the more complex ones to a human agent, including a proposed solution. In e-commerce, an AI agent can monitor the entire lifecycle of an order, respond to questions like “where is my parcel”, change delivery addresses, or handle returns.
In a B2B environment, an AI agent can assist sales reps, prepare materials before a meeting, search for information about a company, summarise communication history, and suggest next steps. In internal processes, an AI agent then functions as a smart assistant that can answer questions about internal policies, processes, health and safety documentation, or HR tasks.
The better an AI agent is connected to your systems and data, the more routine work it can take over. Take a look at more use cases for AI agents that we describe on our AI agent page.
The biggest advantage of an AI agent is that it works continuously and the only thing that gets tired are the servers. An AI agent can handle a large number of requests at once and maintain consistent quality. It saves employees time, allowing them to focus on more complex tasks.
Another advantage is context. An AI agent can remember the course of a conversation, use a customer’s history, and combine information from multiple systems. This is the difference compared to a simple chatbot based only on decision trees. Another benefit is scalability. When the number of enquiries or orders grows, an AI agent can easily be “scaled up” by increasing capacity, without having to hire new people.
For management, it is interesting that an AI agent generates data about what people ask, where processes get stuck, and which requests repeat. Thanks to this, the company can further optimise its workflow. Combined with an AI chatbot on the website and internal AI tools, a complete ecosystem of smart automation is created.
An AI agent is not a magic wand. It has its limits and it is good to be aware of them. The basic disadvantage is that an AI agent relies on the data it has available. If internal documents are outdated, processes unclear, or systems messy, even the best AI agent will get lost in them.
Another limitation is that language models sometimes generate answers that sound confident but may not be correct. That is why it is necessary to design an AI agent so that it has as many verified sources as possible and clear rules for when it should rather hand the case over to a human.
In sensitive fields such as finance, healthcare, or law, it is important to address security, auditability, and access to data properly. Another disadvantage can be the initial investment into process analysis and integrations.
An AI agent has the highest value where it is well integrated with systems and has clearly defined scenarios for when it should decide on its own and when it should only assist.
The price of an AI agent depends on how complex it needs to be, how deep the integrations are, and how much traffic you expect. A simpler AI agent that works similarly to an advanced AI chatbot without complex integrations can cost tens of thousands of Czech crowns.
A more advanced AI agent connected to a CRM, e-shop, warehouse system, and other tools typically falls into higher price ranges depending on the scope of analysis, development, and testing. On top of that, there is usually a monthly operating fee. This covers the costs of using language models, infrastructure, monitoring, and maintenance.
It is important to look not only at the price of an AI agent but mainly at the return on investment. If an AI agent saves the work of one or more people, reduces errors, and speeds up response time to customers, the investment often pays off very quickly.
Companies therefore compare the cost of an AI agent with the cost of manual work or expanding the team. To get an accurate price for an AI agent and its ROI for your company, the best option is a short consultation – just click the button below and get in touch with us.
Choosing the right AI agent always starts with your processes, not with technology. First, it is a good idea to list the specific tasks the AI agent should handle. Should it take care of customer support, evaluate inquiries, help sales reps, or automate administration? The right architecture and type of AI agent are then chosen based on that.
The level of integration is important. If an AI agent works with orders, it should be connected to the e-shop and invoicing system. If it is supposed to answer employees’ internal questions, it needs access to internal documents and policies.
When choosing a provider, watch whether they can design not only the AI agent itself but the whole ecosystem of integrations, legal requirements, and security. A good AI agent should also be easily extendable so it can later learn new tasks.
It pays to test a prototype on a smaller use case first and then gradually expand the AI agent to the areas where it brings the most value.
An AI agent is the next evolutionary step after classic chatbots and simple automation. While an AI chatbot mainly answers questions, an AI agent understands the goal, plans steps, calls tools, and carries tasks through to completion. It uses artificial intelligence, data sources, and integrations with company systems to actually take over part of the everyday workload.
In practice, an AI agent is used for customer support, order automation, document processing, supporting the sales team, or internal employee assistance. It brings time savings, better access to information, and the ability to scale services without having to aggressively expand the team.
At the same time, it has limits related to data quality, security, and process design. When we ask “what is an AI agent”, we are really talking about a smart digital colleague that complements people rather than replacing them. Companies that learn how to use AI agents effectively gain a significant competitive advantage in both efficiency and quality of service.