AI agents: what they are, why companies need them, and why they aren't just a „better chatbot"

Not long ago, most conversations about artificial intelligence started with a simple question: „What can ChatGPT do?". Today the question is increasingly different: what can AI do for us - or together with us?

This is exactly where AI agents come in. It sounds futuristic, a little like something from a tech keynote, but in practice it is a fairly concrete shift. An AI agent is not just a model that answers a question. It is a system that can understand a goal, plan the steps, use tools, fetch data, perform an action and come back with a result.

So not just: „write me an email".

Rather: „check the latest correspondence with the customer, find the order status in the CRM, draft a reply, suggest the next step and save a note in the system".

This still requires human control. But the difference is enormous.

What exactly is an AI agent?

Put simply: an AI agent is a system based on a language model that can carry out a multi-step task using the available tools and data.

An ordinary chatbot answers a question in a chat window. An agent can go further. It can check information in a knowledge base, search documents, run code, compare data, plan a task, send a message, update a record in the CRM or hand the case over to a human.

It is a bit like the difference between someone who gives advice and an assistant who can actually do part of the work.

A good agent usually consists of several elements:

• a language model that understands the instruction and makes decisions,
• instructions - the rules defining what the agent should do and what it must not do,
• access to data, for example documents, a CRM, a knowledge base, a mailbox or a ticketing system,
• tools that let the agent perform specific actions,
• memory or task state, so it does not lose context,
• and control mechanisms that decide when a human has to approve the result.

That last point is especially important. An agent without control can be fast, but not necessarily safe. An agent with well-designed boundaries can genuinely take work off a team's shoulders.

AI agent vs. chatbot - where is the difference?

A chatbot usually works reactively. The user asks, the chatbot answers. It can be very helpful, but its role generally ends with the conversation.

An agent works in a more task-oriented way. It is given a goal and tries to achieve it.

An example?

A chatbot will reply: „To file a complaint, please fill in the form". An agent can check the customer's data, find the order, assess the type of case, prepare the request, fill in the form, draft a reply and pass it to a consultant for approval.

A chatbot will say: „Here is a summary of the document". An agent can read the document, compare it with the previous version, point out risky clauses, prepare a list of questions for the lawyer and save the result in the project folder.

A chatbot will help write a snippet of code. An agent can analyze the error, review the repository, propose a fix, write tests and prepare a pull request.

That is exactly why agents matter so much. It is not about a more impressive conversation. It is about moving from generating answers to doing work.

Where are AI agents used?

The most sensible use cases appear where a task has several steps, requires access to information and is repetitive but not fully mechanical.

Customer service

This is one of the most natural areas. An agent can help a consultant understand a case faster, summarize a customer's history, find the right policy, draft a reply or route the request to the appropriate department.

The best deployments are not about AI pretending to be human and solving everything on its own. A better model is one in which the agent does the boring part of the work: searching, organizing, summarizing, preparing. The human stays where judgment, empathy, an exception to the rule or accountability is needed.

Sales and marketing

An agent can analyze leads, prepare research about a company, create a first version of an offer, personalize messages, plan follow-ups or check which content best fits a specific customer segment.

In marketing, agents can help plan campaigns, create variations of copy, analyze results, monitor competitors and organize insights from many sources. But again: this should not mean automatic content production without oversight. The more a brand relies on trust, the more it needs a human at the final decision.

HR and recruitment

In HR an agent can support writing job ads, answering employee questions, organizing documents, planning training or preparing onboarding materials.

You have to be very careful, however, with recruitment and employee assessment. If an agent starts recommending whom to hire, promote or reject, we enter a high-risk area. Here you need clear rules, human control, documentation and awareness of potential bias in the data.

Finance and administration

Agents work well with documents: invoices, reports, contracts, statements, forms. They can extract data, compare items, point out gaps, prepare summaries and help with compliance checks.

In a large organization this is a huge time saving. But at the same time you have to remember that an agent operating on financial data cannot have unlimited access and freedom of action. Otherwise it is easy to make an error that no one notices, because „the system did it itself".

IT and programming

This is one of the most dynamic areas today. Agents help write code, test, document, analyze bugs, refactor old systems and prepare changes in repositories.

For developers an agent can be something like a very fast technical assistant. But it does not replace the quality process. Code generated by AI still has to be reviewed, tested and assessed for security.

In practice, the best use of agents in IT is not blind trust but shortening the path from problem to proposed solution.

Company knowledge and documents

In many companies the knowledge exists - it is just hard to find. It sits in emails, folders, presentations, policies, notes, old tickets and conversations. An agent can help search this knowledge, connect information and answer employees' questions.

That sounds simple, but it can change the way an organization works. Instead of asking five people where the current document is, an employee can ask the agent. Instead of reading dozens of pages, they can get a summary and links to the sources.

The condition: the agent must use current, approved data. Otherwise it will only elegantly recount outdated things.

How are AI agents built?

Building an agent does not have to start with a big technology project. It is best to start with a simple question: which specific process do we want to improve?

Not „let's make an agent for the sales department". Better: „let's make an agent that prepares research about a potential customer before a sales call".

Not „let's make an HR agent". Better: „let's make an agent that answers employees' questions about leave, benefits and onboarding, using only approved documents".

A well-designed agent usually comes together in a few steps.

First, you choose the task. It should be repetitive, well described and valuable enough for automation to make sense.

Then you describe the process: what the inputs are, what the decisions are, what data is needed, what the result is and when a human is required.

Next you choose the model and tools. An agent can use a document search engine, a database, a CRM, a calendar, email, a ticketing system, spreadsheets, a code repository or external APIs.

Then you write the instructions. This is a very important stage. The agent has to know its goal, the tone it should use, which sources it may use, what it must not do, when it should refuse and when it should hand the case to a human.

Then comes testing. Not only on ideal examples. You have to check edge cases: missing data, conflicting information, out-of-scope questions, attempts to force inappropriate behavior, sensitive data, faulty instructions.

Only at the end does the agent reach users. And here too it is best to go gradually: first a small group, then a larger team, then a wider rollout.

A simple agent is often better than a „brilliant" one

There is a lot of hype around AI agents today. It is easy to believe that the best agent is one that plans on its own, decides on its own, uses many tools on its own and performs the task from start to finish on its own.

In practice, simpler solutions often win.

An agent with one clear goal, limited access to data and well-described rules is often far more useful than a complex system that „can do anything". In its materials on building agents, Anthropic notes that effective deployments often rely on simple, composable patterns rather than the most complex frameworks.

That is an important lesson for companies. You do not have to build a digital employee that replaces half a department right away. It is better to start with an agent that solves one specific problem and does it well.

What tools are used to build agents?

The market is developing very fast. Major providers offer their own platforms and SDKs for building agents.

OpenAI is developing the Agents SDK and tools for building agentic applications in which an agent can plan, use tools and maintain task state. Anthropic publishes practical patterns for building agents and is growing the ecosystem around Claude. Google is developing Agentspace and enterprise solutions, including no-code tools for creating agents and agents available within Google Cloud. Microsoft strongly promotes a vision of working with agents in organizations, especially in the context of Microsoft 365 and Copilot.

There are also open-source frameworks and tools such as LangChain, LlamaIndex, CrewAI or AutoGen. They let you connect models with tools, data and processes. They are useful for technical teams, but they do not solve the most important problem: what the agent should do, why, on what data and with what level of control.

Technology is only part of the puzzle. The architecture of the process is more important.

The biggest risks of AI agents

An AI agent is more powerful than a chatbot, so it also has greater potential to make a costly mistake.

The first risk is faulty action. The model may misunderstand an instruction, draw a wrong conclusion or use outdated data.

The second risk is excessive permissions. If an agent has access to email, the CRM, documents and financial systems, you have to define very precisely what it can do on its own and what requires approval.

The third risk is data security. An agent should not retrieve or disclose information the user has no right to access. This is especially important in large companies, where documents are scattered and permissions can be complex.

The fourth risk is automation without accountability. If an agent makes a bad decision, the company cannot say: „the AI did it". Responsibility still lies with the organization.

The fifth risk is a lack of transparency. The user should know when they are dealing with an agent, what actions the agent took and when a human approved the result.

Where should the human stay?

The best agent deployments do not remove the human from the process. They move the human to where their role matters most.

• An agent can gather data. A human assesses whether the conclusion makes sense.
• An agent can prepare a draft reply. A human approves it in a difficult case.
• An agent can flag risks in a contract. A lawyer decides what to do about them.
• An agent can write code. A developer checks security and quality.
• An agent can prepare a candidate analysis. A recruiter is responsible for the decision and its justification.

This is a healthy model: AI does part of the work, but the human retains responsibility where the stakes are high.

Why do AI agents fit the AI TrustCERT theme?

Because agents very quickly reveal whether an organization truly understands the responsible use of AI.

With an ordinary chatbot, the risk often ends with a wrong answer. With an agent, an error can mean sending the wrong message, updating the wrong record, disclosing data, performing an unauthorized operation or automating a decision that should belong to a human.

That is why agents require maturity. You have to know where they operate, what permissions they have, what data they use, who supervises them, how they are tested, how their actions are logged and when a human has to step into the process.

AI TrustCERT can be a very practical reference point here. The goal is not to block innovation. The goal is to keep companies from deploying agents on a „let's see what happens" basis. In an organization an AI agent should have its place, its rules, an owner and limits.

How to start in a company?

It is best to start with a small, well-described use case.

A good first agent might be a system that summarizes customer requests and proposes a case category. Or an agent that prepares research before a sales meeting. Or a tool that answers employees' questions from an internal knowledge base. Or an agent that supports the IT team in analyzing bugs.

A weak first idea is an agent meant to independently make decisions about customers, candidates, payments or legal matters.

To begin with, it is worth following a simple rule: the greater the impact on people, money, data or the company's reputation, the more control and the less automation.

Summary

AI agents are the next stage in the development of artificial intelligence at work. It is no longer just about generating text, answers or summaries. It is about systems that can carry out multi-step tasks, use tools and operate within specific business processes.

This can bring companies a lot of value: less manual work, faster service, better access to knowledge, more efficient programming, simpler reporting and more automation. But the same technology requires sensible boundaries.

An AI agent without rules is a risk. An AI agent with a clear goal, access control, human oversight and good testing can become genuine support for an organization.

So the most important question is not: „should we have AI agents?". A better question is: which tasks are repetitive, important and well-described enough for an agent to help - and how do we do it in a way that can be trusted?