AI agents: from the idea to a first agent that uses real tools

The chatbot you know waits for your question, writes an answer, and stops. But what if you asked it: “Find three suitable programming courses for my son, compare their schedules, add the best one to my calendar, then send me a summary”? That is not one answer but a chain of steps that needs searching, deciding and acting. This is where the idea of an AI agent comes in: a system that uses a language model to plan, choose the right tools, carry out the steps and check the result until it reaches the goal. In this article we explain the idea clearly, separate it from a chatbot, lay out a practical plan for building a first agent, and talk frankly about the risks and limits.

From chatbot to agent

A chatbot generates text. An agent uses text to make decisions and carry out actions in the digital world: reading a file, searching a database, sending a message or running code. The essential difference is that an agent works in a loop: it thinks about the next step, carries it out with a tool, observes the result, then decides what comes next – until the goal is complete or it needs human help.

To understand this, remember what we explained in large language models: the model itself cannot open a website or send an email. What happens is that the model writes a structured “request” to use a tool, then another program actually carries out that request and returns the result to the model.

The agent loop: think, act, observe

A simple example

Suppose the task is: “What is the average mark in the last maths test?” The agent might think: “I need the marks file.” So it calls the file-reading tool. It notices the file is a table with a marks column. It calls the calculation tool to find the average. It observes the result, then writes the answer for the user, explaining where it came from. Every step here came from a decision the model made based on what it saw.

The five parts of any agent

How does tool calling work?

When you build an agent, you describe each available tool to the model: its name, what it does, and the information it needs. For example, a tool called “find a course” needs “topic” and “age”. When the model sees that a question needs this tool, it does not write an ordinary answer; it writes a structured request naming the tool and the input values. Your program checks the request, carries it out, and returns the result to the model so it can continue.

This point matters for safety: the program acts, not the model. That means you can put strict rules in the program – such as removing a delete tool altogether, or requiring human approval before any message is sent.

The following short video from IBM Technology explains the idea of agents concisely:

When do you need an agent, and when not?

Not every project needs an agent. If the task is question and answer, a good model with a clear prompt is enough. If the steps are fixed and known in advance, traditional automation – a program that follows set steps – is simpler, cheaper and more predictable. An agent helps when the steps change depending on what it discovers along the way, and when it is hard to write every possibility in advance.

Examples of agents in education

To make the picture concrete, here are three realistic agents a school or academy could build, with the tools each one needs:

The personal revision agent

It reads a student’s quiz results, identifies the topics where they make the most mistakes, suggests suitable exercises from the question bank, and reassesses their level after they solve them. Its tools: reading results, searching the question bank and saving a revision plan. It needs no permission to change marks.

The enquiries agent

It receives parents’ questions about dates, fees and courses, answers from the official documents, and passes cases it cannot answer to the right member of staff with a short summary of the conversation. Its tools: retrieval from documents and creating a follow-up ticket.

The materials agent

It helps a teacher turn a written lesson into multiple-choice questions, revision cards and a one-page summary. Its tools: reading the lesson file and saving drafts. The teacher reviews everything before it is published to students.

Notice that each agent here has a specific task and few tools, and that the final decision on anything important stays with a person. That is the design that makes agents useful and safe at the same time.

Risks and safety rules

The more permissions an agent has, the more damage a mistake can do. These basic rules must be respected:

• Least privilege: give the agent only the tools it needs. An agent that summarises email does not need permission to send.
• Human approval for important actions: payments, deletions, sending to outside parties and changing data all need confirmation from a person.
• Beware of hidden instructions: a web page or document the agent reads may contain text that tries to steer it into doing something it should not. This is called “prompt injection”. Treat all outside content as data, not commands.
• Logs: keep a record of every tool call and its result, so you can understand what happened when something goes wrong.
• Limits on the loop: set a maximum number of steps and a cost limit, so the agent cannot spin round endlessly.

What about multi-agent systems?

You will sometimes read about systems in which several agents cooperate: one searches, another writes, a third reviews. The idea is attractive, but it adds complexity, cost and new points of failure. The practical rule is to start with one agent that does its job well, and to split the work across several agents only when testing shows that it genuinely improves the result.

A practical plan for building your first agent

1. Choose a small, specific task: such as “summarise and categorise the day’s enquiry messages”. Avoid starting with something broad like “run customer service”.
2. Define the tools: read-only at first. Add writing tools later, with human approval.
3. Write clear instructions: use what you learned in prompt engineering – the role, the limits and the output format.
4. Prepare test cases: twenty real cases whose correct outcome you know.
5. Watch and improve: review the logs, find where the agent goes wrong, and adjust the instructions or tools.

To build a real agent you will need Python programming skills and some experience working with APIs. You can start with the programming courses among the academy’s courses, then move on to the artificial intelligence course, where we meet these ideas and apply them. To organise your journey, read a beginner’s map for learning AI or try the Find your path tool, and you will find further resources in the learning library.