What an AI agent is, in short
An AI agent is a system that takes a goal and chooses for itself the steps to reach it. It doesn't stop at a written answer: it looks up information, uses real tools such as email, an ERP or a browser, checks what came out and keeps going until the task is done or until it reaches a point where it needs a person. Anthropic draws a useful distinction between workflows, where the steps are written in code in advance, and agents, where the model itself decides the order of the steps and the tools it uses.
In its practical guide, OpenAI describes agents as systems that carry out tasks on the user's behalf with a high degree of independence. The term, however, is used far more loosely than it should be. In June 2025 Gartner warned that many vendors practice “agent washing”, rebranding assistants, chatbots or RPA automations without real agent capabilities as agents, and estimated that only about 130 of the thousands of vendors presenting themselves this way actually offer agents. It's worth checking what a product really does, not what it is called.
How an AI agent works: goal, steps, tools, verification
Behind an agent there is a language model, but the model on its own only generates text. The agent appears when that model is placed in a loop: it receives the goal and the context, proposes the next step, calls a tool, reads the result and decides what to do next. Tools are concrete connections to the company's systems: a search in the customer database, reading an invoice, creating a document or sending an email. Many connect today through MCP, an open standard launched by Anthropic at the end of 2024 that lets models access tools and data in a common format.
The part that makes the difference in production is verification. A well-built agent checks the result of every step, recognizes when the task is finished and stops when something doesn't add up, instead of guessing. OpenAI's guide stresses the same point: on failure, the agent must be able to halt execution and hand control back to a person. In practice this means written rules, clear limits on what it is allowed to change and a log in which every action can be checked afterwards.
- 01Receives the goal and the context: what has to be achieved and which data it starts from.
- 02Plans its steps and picks the right tool for each one.
- 03Carries out the step through the tool: reads, searches, fills in or sends.
- 04Checks the result and corrects course if something doesn't add up.
- 05Stops at the end or asks a person for approval before steps with impact.
AI agent, chatbot, generative AI and RPA: what's the difference
The confusion comes from the fact that all four use, in some form, the same kind of technology. The difference lies in what they are allowed to do. A chatbot answers questions, usually based on rules or a knowledge base. Generative AI, like a ChatGPT-style assistant used in conversation, creates text, images or code on request, but the result goes to a person, who decides what to do with it. RPA automation executes fixed steps, identical every time, and gets stuck when the format changes.
The agent combines the useful parts: it understands freely worded requests, like generative AI, but it also acts inside systems, like RPA, without needing a fixed route. That is also the difference behind the discussions about agentic AI and generative AI: the first does, the second produces content. The price of flexibility is that an agent can make mistakes in less predictable ways than a script. That's why, where the steps are always the same, classic automation often remains the safer and cheaper choice.
| Type | What it does | Where it fits |
|---|---|---|
| Chatbot | Answers questions based on rules or a knowledge base | Frequent questions, simple bookings |
| Generative AI | Creates text, images or code on request, and a person decides what to use | Drafting, summaries, ideas |
| RPA | Repeats fixed steps in applications, identically every time | Stable processes with a constant format |
| AI agent | Chooses its steps, uses tools and checks the result | Tasks with variations, across several systems |
Examples of AI agents: what they can take over in a company today
The best-known public examples are agents for working on a computer and for programming. Claude Cowork, from Anthropic, works with the user's files and runs scheduled tasks, while Codex, from OpenAI, writes and tests code based on a requirement. In companies, useful agents are usually narrower: each has a single responsibility, access only to the systems it needs and clear rules about when to stop and ask a person.
The examples below have one thing in common: they start from data that already exists in digital form and end with a proposal that a person approves. An agent that prepares the booking of an invoice doesn't also post it on its own, and one that drafts the reply for a customer doesn't send it without review until the results are consistently good. Autonomy is extended gradually, as mistakes decrease and the rules become clearer.
- 01Incoming invoices reads the invoice from e-Factura, matches it to the order and prepares the booking for approval.
- 02Customer support finds the order, checks the delivery status and proposes a reply to the operator.
- 03Sales updates the CRM after a meeting and prepares the follow-up message.
- 04Internal reports gathers figures from the ERP and from spreadsheets, flags the differences and sends the summary.
What can go wrong with an AI agent and how to protect yourself
The most discussed case of 2026 shows why limits matter. On 16 July, Hugging Face announced that an autonomous AI agent had compromised part of its infrastructure. On 21 July, OpenAI and Hugging Face confirmed in a joint statement that the agents came from an internal OpenAI cybersecurity test, intentionally run without the usual safeguards, and that they had escaped the isolated environment in which they had been launched. The situation is extreme, but the lesson applies to any company: an agent does what its tools and the access it has been given allow.
The usual risk in a company is more mundane: an agent with too many rights that performs a wrong action, quickly and many times over. In 2025 Gartner estimated that over 40% of agentic AI projects will be canceled by the end of 2027, because of costs, unclear value or insufficient risk controls. Protection starts with minimal permissions, an environment that truly isolates the agent, a log for every action and human approval for anything that can't be undone. We've covered separately how to set the permissions of AI agents in a company.
How ready companies in Romania are for AI agents
Eurostat data published in December 2025 shows that 20% of companies in the European Union with at least 10 employees used AI technologies in 2025, up from 13.5% a year earlier. Romania had the lowest share in the Union, 5.2%, while Denmark reached 42%. The gap doesn't come from a lack of access to models, which are the same for everyone, but from how ready the processes and data that an agent would have to use actually are.
For a Romanian company, this is good news rather than bad news. A lot of operational data is already digital because of legal obligations, from the invoices in e-Factura to the SAF-T returns, and an agent needs exactly this kind of structured data to work safely. The weak point is usually somewhere else: unwritten processes, exceptions solved from memory and systems that don't talk to each other. That's where most time is lost in a first project, not in choosing the model.
How to start with an AI agent without risking the processes that work
The first agent should take over a single repetitive process, with rules you can write down and a result you can measure. Choose something where a mistake shows up quickly and is easy to fix, not the payment flow or the relationship with your most important client. Describe the steps the way they are done today, exceptions included, and define in advance what success means: time saved, fewer errors or requests resolved without intervention.
After the pilot, expansion follows the same pattern: one more process, the same access rules, the same measurement. That's also how we work when we build AI agents for companies: a short discovery, a first flow put into production with human approval and only then more autonomy, where the results justify it. If you don't yet know which process to choose, we've written a separate guide on which processes to automate first with AI.
- 01Choose a repetitive process with clear rules and data that is already digital.
- 02Write down the steps and exceptions the way the work is done today.
- 03Set the minimum access and the steps that require human approval.
- 04Run a pilot on real volume and measure the result.
- 05Extend autonomy only where the results are consistently good.
Sources and further reading.
- 01Anthropic — Building effective agents
- 02OpenAI — A practical guide to building agents
- 03Gartner — Over 40% of agentic AI projects will be canceled by end of 2027 (25 June 2025)
- 04Eurostat — 20% of EU enterprises use AI technologies (11 December 2025)
- 05OpenAI and Hugging Face — statement on the security incident during model evaluation (21 July 2026)
- 06Hugging Face — Security incident disclosure, July 2026
Frequently asked questions
What is an AI agent?
An AI agent is a system based on a language model that takes a goal, chooses its own steps and uses real tools, such as email, an ERP or a browser, until the task is done. Unlike a chatbot, it doesn't only answer: it acts and checks its result.
What's the difference between generative AI and agentic AI?
Generative AI produces content on request, and a person decides what to do with it. Agentic AI uses the same kind of model, but gets access to tools and carries out steps in a process on its own, checking the result. The first writes, the second does.
Is ChatGPT an AI agent?
In ordinary conversation, ChatGPT is a generative AI assistant: it answers, and you decide what to do with the answer. When it gets access to tools and can carry out steps on its own, as happens in agent mode, it comes close to the definition of an agent. What makes the difference is what it is allowed to do, not the product name.
Are there free AI agents?
There are open-source frameworks and free versions of some products with agent features, good for testing. In a company, the real cost is not the license but connecting to internal systems, setting permissions, testing on real cases and supervision in the first months.
Can an AI agent get out of control?
The OpenAI and Hugging Face case of July 2026 happened during a security test intentionally run without the usual safeguards. In a company, the realistic risk is an agent with too many rights that makes mistakes quickly. Minimal permissions, isolation, an action log and human approval reduce this risk.
Which processes are a good fit for an AI agent?
Repetitive processes with constant volume, data that is already digital and rules that can be written down, but with enough variation that classic automation gets stuck. Examples: processing incoming invoices, replies to recurring customer requests or reports that pull data from several systems.
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