# What is the Difference Between an Agent and a Chatbot? | leren.llmnet.nl

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# What is the Difference Between an Agent and a Chatbot?

By Ivo Donker - 6 August 2026

The essential difference between a chatbot and an AI agent is that a chatbot only reacts to what the user enters, while an agent independently plans and executes a series of steps to achieve an overarching end goal.

In practice, this means that a chatbot waits until a human asks the next question or gives an instruction. An agent, on the other hand, receives a specific task, formulates its own plan, may call external tools to retrieve information or perform actions, checks the intermediate results, and adjusts its next steps until the task is completed. The nuance therefore lies not directly in the language model used, but in the control structure built around the model.

## The chatbot: how a conversation turn works

A classic AI chatbot is built around a question-and-answer pattern, also known as a 'single turn' or single turn interaction. When a user types a text message, the system follows a fixed path:

 
- Input: The user sends a prompt to the application. The application may add earlier messages from the conversation history to provide context.
 
- Model processing: The large language model (LLM) receives this full text and calculates, based on statistical patterns, which words form the most logical answer. Read more about this process in the explanation of [tokenization](/en/tokenisatie-uitgelegd) and [inference](/en/inference-uitgelegd).
 
- Answer: The model generates the text and the application displays it on the user's screen. At that exact moment, processing stops completely.

A chatbot by definition undertakes nothing on its own after the answer has been generated. The system has no active 'standby mode' in which it keeps thinking or processes tasks in the background. Only when the user presses the send button again is the model called again with the updated conversation history. If the given answer is incomplete or incorrect, the user must ask the follow-up question themselves to steer the process. The initiative therefore lies entirely with the human.

## The agent: the loop principle and autonomous steps

An AI agent uses the same type of language model as a chatbot, but is set up within an iterative control loop (often called an agentic loop ). Instead of generating a direct textual answer to the user, the model takes on the role of decision-maker in an ongoing process.

The operation of an agent invariably follows the steps below:

 
- Determine the goal: The user gives an overarching task, such as: "Analyze last quarter's sales figures and email a summary to the team."
 
- Plan: The model analyzes the goal and breaks it down into logical intermediate steps. It decides which action is needed first.
 
- Action via a tool: The agent selects a specific tool and formulates a command to call this tool, for example reading data from a database.
 
- Observing the result: The application executes the command and returns the result (the data from the database) to the model as a new observation (observation).
 
- Reassess: The model looks at the result. Has the goal been achieved? If not, what follow-up step is now needed? This process repeats until the end result is achieved or the pre-set maximum number of steps is reached.

Thanks to this loop, an agent can absorb errors. If a requested file does not exist, the agent reads the error message as an observation, adjusts its plan, and tries an alternative search, without the user needing to intervene in the meantime.

## What tools mean in concrete terms

A common misconception is that the language model independently browses the internet, opens files, or executes code. This is technically not the case. A language model is a purely text-processing system: it receives text and generates text.

When we talk about the use of 'tools' (tool use or function calling), the model generates a structured text message (usually in JSON format) that indicates which tool it wants to use and with which arguments. The software environment around the model reads this message, performs the actual action outside the model, and feeds the output back to the model as text.

Examples of tools that can be connected to an agent:

 
- Search: Calling a search engine or an internal search index to retrieve current information. For retrieving internal documents, also see the guide on [RAG for beginners](/en/rag-voor-beginners).
 
- Reading and writing files: Opening a PDF document, analyzing a CSV file, or saving a new text file to disk.
 
- Performing calculations: Forwarding a mathematical formula to a Python interpreter or calculator to prevent calculation errors (hallucinations).
 
- Calling external systems: Sending a message via an API to a CRM system, scheduling an appointment in a calendar, or updating a status in a database. To understand how these integrations work technically, you can consult the documentation at [api.llmnet.nl](https://api.llmnet.nl/en/).

## Comparison Table: Chatbot versus Agent

To lay out the differences clearly side by side, the table below compares the two concepts on the most important operational characteristics:

 
 
 
 Characteristic | 
 AI Chatbot | 
 AI Agent | 
 

 
 
 
 Initiative | 
 Exclusively human (reactive) | 
 Autonomous within the given task (proactive) | 
 

 
 Model calls per task | 
 Exactly one call per user message | 
 Multiple consecutive calls within a loop | 
 

 
 Access to external systems | 
 Usually limited or absent | 
 Broadly integrated via tools and APIs | 
 

 
 Predictability | 
 High (direct result for a specific prompt) | 
 Lower (the chosen path can vary per run) | 
 

 
 Cost per task | 
 Low and directly predictable | 
 Higher and variable, depending on the number of steps | 
 

 
 Fault sensitivity | 
 Limited to one answer (hallucination in text) | 
 Cascading errors possible due to incorrect intermediate choices | 
 

 
 Primary use case | 
 Answering questions, rewriting text, brainstorming | 
 Complex workflows, data analysis, automatic task execution | 
 

 
 

## The gray area between chatbots and agents

In practice, the boundary between a chatbot and an agent is not always sharply defined. Many software vendors use the term 'agent' as a marketing label for applications that are fundamentally still chatbots.

A chatbot that performs a one-time web search while generating an answer (known as grounding or web-search RAG) is, strictly speaking, not yet an agent. In that case, there is no autonomous control loop or goal evaluation: the application follows a fixed, programmed, linear path (search -> add text -> generate answer) and then stops.

 Where is the line? A system crosses the threshold to become an agent when the language model itself is allowed to decide or whether it uses a tool, which tool it chooses, and based on the result can independently decide to take an extra step that was not predetermined.

There are various hybrid forms. For example, there are hybrid systems in which a fixed workflow (a series of pre-set steps) is combined with small agentic decision points at specific points. More information about maintaining the right context across multiple steps can be found in the article on [memory in LLM applications](/en/geheugen-in-llm-apps).

## Why agents are technically and operationally more difficult

Although an agent that performs tasks independently sounds attractive, this architecture brings significant practical challenges:

 
- Cascading errors: If an agent makes an incorrect assumption in step 1 of a process or misinterprets a faulty search result, it builds on this error in step 2. After four or five steps, the end result can deviate completely from the intended outcome.
 
- Rising costs and latency: Because an agent must make a new model call for every step in the loop, latency (wait time) and token costs increase quickly. A task that requires ten steps costs more than ten times as much computing power and time as a single chatbot answer.
 
- Testability and determinism: A chatbot gives output for a specific question that can be checked almost immediately. An agent can choose a different route to reach a solution on each run. This makes testing software updates and quality assurance challenging.

## When do you make which choice?

When designing or purchasing an AI solution, it is advisable not to automatically choose the most complex option. Base the choice on the type of task:

### Situation 1: Information provision and support

Scenario: You want to give employees the ability to ask questions about internal policy documents or protocols.
Recommendation: Choose a chatbot (optionally equipped with RAG). The user retains control, the answers are immediately visible, and the costs stay low and manageable.

### Situation 2: Automated data processing

Scenario: Every week, data from multiple separate systems (e.g., email, Excel, and a CRM) must be collected, compared, and combined into a report.
Recommendation: Choose an agent. Because the task requires multiple consecutive actions and check steps, an autonomous loop saves considerable manual work here.

### Situation 3: Assisted customer service with actions

Scenario: Customers want to ask questions about their order, but also need to be able to change their address or request a return label.
Recommendation: Choose a hybrid form. Let the system start as a chatbot for informational questions. When the customer wants to perform an action, the system switches to a tightly scoped agent function that is specifically authorized for that specific administrative action.

## Next steps

Now that you understand the difference between the reactive operation of a chatbot and the autonomous loop principle of an agent, you can dive deeper into the underlying techniques:

 
- [Chain of Thought](/en/chain-of-thought) – Discover how reasoning steps help models make more complex decisions.
 
- [Guardrails explained](/en/guardrails-uitgelegd) – Read how to make agents and chatbots operate within safe boundaries.
 
- [Learning path for beginners](/en/leerpad-beginners) – View an overview of all the basic concepts about AI and language models.

© 2026 llmnet.nl · Ivo Donker