If you ask a normal AI chatbot something like “what is the weather in Mumbai right now?”, it will probably give you an answer straight away. It might even sound very confident while doing it. But there is an important thing to understand here: if that chatbot does not have access to live weather information, it has not actually checked the weather. It is just generating an answer from the information and patterns available to it. Now take the same question and give the AI access to a weather tool. The interaction becomes completely different. Instead of having to rely on what it already knows, the AI can request the current weather, receive the result and then use that information to answer you. This is basically the point where the difference between a regular chatbot and an AI agent starts to become clear. A normal AI chatbot is mainly built around one simple interaction: you give it something, and it gives you something back. More specifically, you give it text and it generates text in response. Of course, modern chatbots can do a lot with that text. They can explain difficult topics, help with writing, summarise information, brainstorm ideas and even have surprisingly long conversations where they seem to understand what you are trying to say. But underneath all of that, the basic job is still generating a response from the context it has been given. The problem comes when the answer depends on something outside that context. If you ask a chatbot to tell you where your parcel is, it cannot know the current location of your parcel unless it has some way of accessing the company's order system. If you ask it to check today's stock price, it needs current market data. Even something as simple as checking today's weather requires a source of live information. Without access to those things, the model can only produce what it thinks is a suitable answer. It does not have a direct connection to the real-world system you are asking about. This is where AI agents become more interesting. An agent can be given access to tools that allow it to interact with systems outside the conversation. The model itself is still generating language, but now it has another option available to it: it can ask the surrounding software to use a particular tool. The way this happens is actually quite interesting because the AI is not directly running the tool. A developer first defines the functions that the AI is allowed to use. Each function has a name, a description and information about the inputs it expects. For example, a developer could give the model a get_weather(city) function and specify that it needs the name of a city. When the user asks about the weather, the model can recognise that the question matches the purpose of this function and produce a structured request to use it. The application then receives that request and actually runs the function. Maybe it calls a weather API, searches a database or gets information from another service. Once the function has finished, its result is sent back to the model. The model can then use that new information to continue the conversation. Anthropic explains this process in its documentation on tool use, where the model requests a tool, the application executes it and the result is then returned to the model for it to continue working. This is why the term “function calling” can be slightly misleading if you are hearing it for the first time. The model is not sitting there and executing code on its own. It is more like it is saying, “I need this function to answer the user's request, and these are the inputs that should be given to it.” The actual software takes care of running it. OpenAI describes function calling in a similar way, with the model producing structured arguments for a function while the application handles the actual execution. So, going back to the weather example, the process would look something like this. You ask, “What's the weather in Mumbai?” The model understands that it needs current information and decides to use get_weather. It sends a structured request with Mumbai as the city. The application runs the weather function and gets back the current conditions. That result is then given to the model, which can finally tell you the answer. The important difference is that the final response is now based on information that was actually retrieved, rather than something the model simply generated from its existing knowledge. Things get even more useful when an agent has access to several different tools. Imagine you ask it to find a restaurant for four people tonight, check whether there is a table at 8 PM and then send the details to your friend. A regular chatbot could tell you how to do all of this, but it would still leave the actual work to you. An agent with the right tools could potentially search for restaurants, use another tool to check availability and then use a messaging tool once it finds an option that works. If the first restaurant is full, the result of that search can affect what the agent does next. It might try another restaurant instead. This is where the idea of an “agentic loop” comes from. The model doesn't necessarily make one decision and finish. It can receive a task, decide that it needs a tool, use the tool, look at what came back and then decide what to do next. If the task is complicated, this process can repeat several times. Anthropic's explanation of tool use follows this same basic structure: the model requests a tool, the application executes it, the result goes back into the conversation and the model continues from there. There is a pretty big difference between this and simply giving a chatbot access to a search box. The agent can use the result of one action to influence the next action. For example, if it searches for something and the results are not useful, it can search again. If a database says that an item is unavailable, that can change what it recommends. It is the ability to continue working with the results that makes the whole system feel more like an agent rather than just a chatbot with a few extra features. You can already see this idea in applications people use. Take AI coding assistants. A normal chatbot can look at code that you paste into the chat and explain what might be wrong. An AI system with access to your development environment can do considerably more. It can inspect files, find the part of the project that is causing the problem, make an edit and potentially run tests afterwards. The results from those tests can then tell it whether the change worked or whether something else needs to be fixed. Tools that allow models to edit text, run commands or interact with computers are already part of modern AI agent systems. Customer service is another example where the difference is easy to see. A chatbot might tell you that your order could be delayed and ask you to contact support. An agent connected to the company's systems could actually look up your order and see its current status. Depending on the permissions it has, it could also create a support ticket or start another process for you. The AI is no longer only explaining what a person could do. It has access to the system where the action can actually happen. At the same time, this extra ability creates a problem that is easy to overlook. Giving an AI access to tools also means giving it some level of control over the systems those tools are connected to. A wrong answer from a chatbot can be annoying, but a wrong action by an agent can have a real consequence. If an agent can send emails, change files, make bookings or update information in a database, developers have to be much more careful about what they allow it to do. This is why permissions and tool design matter so much. Not every tool should necessarily be available to every model, and some actions may need confirmation from a person before they are carried out. The application is ultimately the part that executes the tool, so the developer still has an important role in deciding what the agent can and cannot do. So when people say that AI agents are the next step after chatbots, I don't think the most useful way to look at it is simply that an agent is “smarter”. A chatbot can already be very good at understanding language and producing useful answers. The bigger change is that an agent is connected to an environment and has ways of interacting with it. The model can understand what the user wants, decide that it needs some outside information or action, request the relevant tool and then use the result to continue. That is what makes tool use and function calling so important. They create a connection between what an AI can say and what the software around it can actually do. A chatbot can explain how you could check a flight, while an agent with access to the right system could potentially check the flight for you. A chatbot can suggest how to fix your code, while an agent with access to the project can actually inspect the files and make the changes. Once you look at it this way, an AI agent is not really some completely different type of intelligence. It is a language model placed inside a larger system, with tools available to it and a way of deciding when those tools are useful. The interesting part is what happens when you keep expanding those capabilities. The question then stops being only about how well the AI can answer a question and starts becoming about what systems it can interact with, what actions it is trusted to take and where humans still need to stay in control.