AI agents and agentic AI: what AI agents are and how they differ from an assistant and a chatbot

AI agent: what it is and what agentic AI means

AI agent is a program you tell what to achieve, not how to do it. It is given a goal, for example “prepare a note on the client before tomorrow’s meeting”, and then works out for itself where to look for information, reaches for tools, assesses whether the result is good enough and decides on the next step, until the note is ready or the agent reaches a point where it has to ask a human. Sounds simple. The devil is in the limits.

Agentic AI is a broader term. It describes a way of using artificial intelligence in which the model doesn’t stop at an answer but plans and acts, and then improves itself in a loop. Looking for a short definition (agentic AI definition)? Here you go: it is AI that carries out multi-step tasks on its own with the help of tools, within limits set by a human.

We set out practical applications in marketing and communications, together with costs, in the article AI agent in marketing and PR. If you are only just sorting out the topic from the basics, start with our guide to AI in business, where agents are one of seven sections.

“Agenci AI” or “agenty AI”: which Polish form is correct

Both forms are heard in Polish companies. The PWN Language Advisory Service (Dr Rafał Sidorowicz, 28 February 2025) recommends the form agenty AI, because it is a computing term that refers to programs, not people. In the same way, Polish uses “klienty poczty” for email programs and “klienci” for people. The Language Advisory Service of the University of Warsaw gives a similar answer, noting that usage is still settling.

In everyday speech, however, the form agenci AI. Nobody will call it a mistake in a sales conversation. We use both in this text, but in technical documents and contracts stick to one, the one your legal department adopts, because inconsistent terminology in a contract with a supplier means needless questions with every annex later on.

How an AI agent differs from an AI assistant and a chatbot

These three terms are often mixed up, including in vendors’ materials. The difference lies not in how “intelligent” the program is, but in who leads the work and what the program can do on its own, without asking anyone’s permission, in the systems it has been given the keys to. The comparison AI agent vs chatbot is easiest to see in a table. It will come in handy with every purchase.

What an agent is made of: model, tools, memory and loop

Every agent has four layers. Language model understands the goal and plans. Tools let it act, i.e. search the internet, read documents or write to a spreadsheet. Memory stores what it has already done and learned, and the loop means that after every step the agent assesses the result and decides for itself what to do next, instead of waiting for your next instruction. If it is to answer on the basis of company documents, searching those documents comes into play as well, which we describe in our article on RAG, i.e. AI working on company knowledge.

For an agent to use tools from different companies, common standards are needed. The most important is MCP (Model Context Protocol, a common way of connecting models to tools and data), developed by Anthropic. In December 2025 MCP, together with AGENTS.md from OpenAI and the goose tool from Block, moved to the new Agentic AI Foundation under the Linux Foundation. According to the foundation’s announcement, more than 10,000 MCP servers have already been published, and the protocol is used by Claude and ChatGPT, among others, as well as Gemini and Microsoft Copilot.

The takeaway for managers? An agent built on open standards is easier to move to another provider. Ask about this when choosing a tool.

Agentic AI in business: examples

The best examples of agentic AI (agentic AI examples) are boring, frequent tasks that are also well described. Here are a few applications we see in mid-sized companies:

• PR. Every day the agent reviews mentions, discards reprints and prepares a page with three issues for the spokesperson to decide on.
• Sales. Before a meeting it gathers information about the client from the CRM, the company website and recent publications. We show what this looks like in sales practice in connection with CRM with AI.
• Customer service. The agent checks the order status in the system and prepares a reply, which a consultant approves. How this differs from a classic chatbot is explained in our article on AI in customer service.
• Marketing. Once a week it compares competitors’ offers and announcements and records the changes in a single spreadsheet.
• Quality control. A separate agent checks whether every figure in the material has a source and a date before the text goes for approval.

Who AI agents in business are for, and in what situations

AI agents make sense for businesses where the team loses hours gathering and organising information and has little time left for decisions and conversations with people. How can you tell? The same reports put together by hand every week. Customer enquiries waiting for someone to read them. Market information scattered across a dozen or so sources that everyone checks on their own, so nobody in the company has the full picture and the board gets three different versions of the same situation from three different people.

An agent won’t help when there is no strategy or the process is unclear. If nobody knows what should reach the customer and when, the agent will only speed up the chaos. In that case, start by putting the rules in order, for example with the basics of good communication with customers. Automation will come later.

How to introduce AI agents in your company, step by step

This order works regardless of the tool. We discuss specific platforms (ChatGPT, Copilot Studio, Google tools, n8n) in our guide on how to build an AI agent.

1. STEP 01Choose one task

   Repetitive, with a clear outcome and measured human working time. One. Not five at once.

2. STEP 02Set the limits

   Write down what the agent can read, what it can write and what it will not do without human consent. This list always includes sending and publishing, as well as payments and deleting data.

3. STEP 03Choose a tool that fits your ecosystem

   A company on Microsoft 365 usually starts with Copilot, a company on Google Workspace reaches for Google’s tools, and one that has many systems from different vendors and doesn’t want to be tied to a single ecosystem chooses an automation platform.

4. STEP 04Test on past data

   Run the agent on data from recent weeks and compare the result with human work. Look at the time, the number of errors and whether the output is usable at all.

5. STEP 05Appoint an owner

   One person reviews the agent’s activity log every week and corrects the instructions when data or procedures change.

How much AI agents cost and what drives the budget

The cost of an agent is the sum of four items. The model itself is often the cheapest. The most expensive part is people’s time for preparation and testing, and then for oversight, which teams usually only remember after the first month, when it turns out that someone has to read the agent’s log every week and correct its instructions.

When an agent pays off, and when it does not yet

An agent makes sense when the task comes back every week, the data sits in one place and someone has time to review the results during the first month. Launch in a quieter period. Not in the week before a campaign or a product launch.

Hold off if the vendor cannot demonstrate the agent on your data or cannot say where the agent stops and who is responsible for its mistakes. That is a warning sign. We discuss the ethical and legal risks separately when writing about AI risks and ethics and on The AI Act in Poland. The EU regulation does not create a separate category for agents. Obligations depend on what the agent is used for.

Frameworks, courses and standards: what a manager should know

Agent frameworks (agentic AI frameworks, ready-made libraries for programming agents) are a matter for the technical team. Google offers ADK (Agent Development Kit), Block the open tool goose, and open-source libraries such as LangGraph and CrewAI are also popular. A manager doesn’t need to know them. What they do need to know is whether the chosen solution uses open standards, where the data the agent reads and writes ends up, and how to switch the agent off in one move when it starts doing something nobody planned.

Looking for a course (agentic AI course)? Choose one that teaches you how to design tasks, oversight and measurement, not just how to click around in a single tool, because tools change every few months. We suggest what to look for when choosing in our piece on AI courses with a certificate. Want to understand first what AI really does in marketing and what it doesn’t? Have a look at the page AI in marketing.

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