What is an AI agent? A plain explanation

What is an AI agent, and what can it do that a chatbot cannot? In short: an AI agent is a language model that does not just answer but acts. You give it a goal, and it breaks that goal into steps, calls tools to carry them out, checks the results and keeps going until the task is done or it needs to ask you something. In this guide you will learn how that works technically, how an agent differs from a chatbot, where agents help in everyday work and which limits and approvals you should set from the start.

Definition: what makes an AI agent

An AI agent is software that combines a language model with three further building blocks:

• A goal. Instead of a single question, the agent receives a task, for example "find out why this test fails and fix the bug".
• Tools. These are clearly defined functions the agent is allowed to call: read a file, search the project, run a command, query a record.
• A loop. The agent plans a step, carries it out with a tool, reads the result and then decides on the next step.

The language model does the "thinking", the tools are the "hands", and the loop turns many small steps into a result. One point matters above all: the agent only ever acts within the limits you set. You decide which tools it has and what it may do without asking.

How an agent differs from a chatbot

A chatbot is a conversation partner. You ask a question, it writes an answer, and everything after that is up to you. If you ask a chatbot how to find a bug in your code, you get an explanation and perhaps a code example. Copying, pasting, testing and fixing are still your job.

An agent takes over exactly those in-between steps. It opens the relevant files, looks for the cause, proposes a change, applies it once you approve and runs the tests. If a test fails, it reads the error message and tries again. At the end it reports what it changed and why.

So the core difference lies in who is responsible for the steps in between. With a chatbot, you make every individual decision. With an agent, you define the goal, the tools and the approvals, and the system works independently within that frame. That is what makes agents useful – and it is exactly why they need clear rules.

Tools and tool calls: how an agent acts

On its own, a language model cannot change anything on your computer or in your systems. It only produces text. Tool calls make it possible for the model to act anyway. The process looks like this:

1. The application tells the model which tools exist, each with a name, a description and the parameters it expects.
2. The model decides it needs a tool and, instead of prose, responds with a structured call such as "read the file src/app.ts".
3. The application checks this call and executes it – or asks you for permission first.
4. The result, for example the file contents or the output of a test run, goes back to the model.
5. Based on that, the model plans the next step.

What matters is that the application always executes the call, never the model itself. The model proposes, your system decides. This means you can log, limit and, if necessary, block every single step.

Two rules of thumb have proven useful. First, keep tools narrow. A function with one clear job and few parameters is easier for the model to use correctly than an all-purpose tool. Second, validate inputs on your side. A path outside the project folder or an unexpected command should never run, however plausible the model's suggestion sounds.

Examples from code and the office

Software development. This is where the value of an agent is easiest to see, because code is easy to check. A coding agent such as A2Agent reads your project, follows the conventions of your repository, plans changes across several files and runs your tests. Typical tasks include fixing a reported bug, adding tests, smaller refactorings or explaining code you did not write yourself. You see every change as a diff before you accept it.

Everyday office work. Outside development, too, many tasks consist of lots of small, repetitive steps. An agent can review incoming requests, sort them by topic and prepare draft replies. It can gather information from documents, reconcile data between two systems or work through a checklist and tell you which items are still open. In this case the tools are interfaces to your systems, for example your mailbox, your ticket system or your inventory software. The principle stays the same: plan, call a tool, check the result, continue.

In both cases the agent does not replace your judgement; it takes routine work off your hands. Whether a result is good enough remains your decision.

Limits and approvals

An AI agent is not an error-free system. If you use one, you should know its limits and set rules from day one:

• Mistakes remain possible. Language models can misunderstand things or make up information. If an agent keeps working with a wrong intermediate result, the error can carry forward. Critical results therefore need a human check.
• Grant rights sparingly. Give the agent only the tools and access the task really requires. Reading files in a project folder is very different from write access to a production server.
• Set approvals deliberately. Decide what is allowed without asking (for example reading files or running tests) and what needs your consent (for example changing files, running commands with side effects or sending messages). A2Agent works with permissions per tool for this.
• Limit cost and runtime. Every step an agent takes means model calls. A monthly limit and a maximum number of steps stop a loop from getting out of hand.
• Keep it traceable. Every tool call should be logged so you can understand later why the agent did something.
• Know where your data goes. An agent sends content from your files or systems to a language model. Check where that model runs and whether the data is used for training.

A good way to start is with a task whose result is easy to check, such as adding tests or writing a summary. Only widen the approvals once you have watched the agent work for a while. That way its independence grows at the same pace as your trust.

Conclusion

An AI agent is a language model with tools that carries out several steps on its own within a clearly defined frame. The difference from a chatbot is not the model but the loop of planning, acting and checking. The technology behind it, tool calls, is well established by now. Whether an agent really saves you work depends mostly on how carefully you define goals, tools and approvals.

If you want to try an agent for your software projects, the A2Agent page has everything you need: a desktop app for macOS, Windows and Linux, a terminal interface and A2Intelligence as a model operated in Germany. To see how agents can support processes across your company, visit AI agents for business.