In short
An AI agent is software that, on the basis of a language model, independently carries out a series of steps to reach a goal. It looks at a situation, works out what needs to happen, uses external tools such as your ERP or your documents to do it, and checks whether the result is right.
The difference from what people generally know as AI is that an agent does not stop at an answer. It takes steps.
What does agentic AI mean?
Agentic AI is the umbrella term for this way of working: AI systems that do not merely produce text but carry out tasks with a degree of independence. The word refers to that independence, not to a particular technology.
What it cannot yet do reliably
Work independently for hours without supervision. The longer the chain, the greater the chance something drifts.
Take decisions where a single error immediately carries financial or legal weight, without human control.
Judge things that appear in no data, such as the relationship with a customer or the mood of a case.
Know what it does not know. A well-built system limits that risk, but it never disappears entirely.
THE DIFFERENCE FROM A CHATBOT
A chatbot holds a conversation. You ask a question, it gives an answer, and the turn is over. What it can say depends on what it learned during training or on a list of preset answers.
An agent gets an assignment rather than a question. To complete it, it may look up data, do calculations, read documents and prepare or carry out actions. It takes several steps in sequence and adapts its approach based on what it finds along the way.
- Chatbot: what is the lead time on this item? An answer from a fixed text.
- Agent: make sure this purchase order is in the system. It reads the document, looks up the customer, matches the item numbers, checks the prices and sets up the order.
HOW AN AGENT WORKS, STEP BY STEP
- Assignment. The agent is given a goal, in plain language or automatically on an event such as an incoming email.
- Plan. It works out which steps are needed and in what order.
- Tools. It uses what has been made available to it: your database, your documents, a calculation function, an external service.
- Check. It tests whether the result matches the assignment, and tries another route if it does not.
- Handover. It delivers the result, or puts it to a person for approval.
That fourth step is what sets an agent apart from simple automation. A classic script always follows the same path. An agent picks its path based on what it encounters.
WHAT AN AGENT NEEDS TO WORK
- Access. Without a connection to your systems it knows nothing about your business. In practice this is the largest part of the work. More on ERP integration.
- Limits. What may it finish on its own, and where must a person approve? An agent without limits is a risk.
- Rights. It should inherit the rights of the user addressing it, so it does not show data that person may not see.
- Context. Your naming, your exceptions, your agreements. None of that is in any model, it has to be brought in.
- Follow-up. A log of what was proposed and carried out, so you can check afterwards what happened.
WHAT AN AI AGENT DOES WELL TODAY
- Read documents, understand them and extract data, even when the layout differs every time.
- Look up information across different sources and bring it together into one answer.
- Make proposals a person only has to review: a quote, a posting, a reply, a schedule.
- Classify work, sort it and route it to the right person.
- Hold conversations in several languages, including by phone.
AN EXAMPLE FROM START TO FINISH
A customer emails a purchase order as a PDF. What happens with a well-built agent?
- The email arrives and the agent recognises the attachment as a purchase order.
- It reads the document: customer name, reference, item numbers, quantities.
- It looks up the customer in the ERP and retrieves the applicable price agreement.
- It matches the customer's item numbers to yours, and flags what it does not recognise.
- It checks the prices against the agreement and signals any difference.
- It sets up the order as a draft and notifies the person responsible.
- Your colleague reviews, adjusts where needed and confirms.
The result is not that nobody looks at it any more. The result is that nobody retypes it any more. See more applications.
TERMS YOU WILL COME ACROSS
- Language model or LLM. The underlying model that understands and produces language. It is a component of an agent, not the agent itself.
- RAG. A method in which the model is first handed the relevant passages from your own documents, so that it answers on the basis of your data.
- MCP. An agreed interface that gives AI applications standardised access to systems, so a separate integration is not needed for every tool.
- Human in the loop. A person who steps in at predefined points, usually to approve.
- Hallucination. An answer that sounds convincing but is wrong. Good integrations and source references limit this considerably.
YEARS OF .NET EXPERIENCE
PROJECTS DELIVERED
SUCCESSFUL ERP INTEGRATIONS
IN-HOUSE SPECIALISTS
Is an AI agent right for your business?
The question is not whether the technology is ready, but whether you have work that keeps coming back, has a clear structure and costs time. If so, there is nearly always something to build. If not, you will not earn back your investment, however impressive the demonstration was. This is how we approach it.
Why companies choose IDcreation
- The Belgian Defence has been our largest client for over ten years. What meets those security requirements will hold up with you.
- No standard package you have to bend to, but software that follows the way you work.
- Our own hosting in Belgium, with monitoring, backups and a single point of contact.
- Since 1996 we have built in Microsoft technology. One strong foundation makes us fast and thorough.
