The main difference: a chatbot answers questions, while an AI agent plans steps and carries out tasks with the tools and permissions you give it. But a chat window does not tell you what is possible behind it: products such as ChatGPT combine conversation and execution. So, AI agent or chatbot: which one does your work need? Below, we compare seven aspects, from completing tasks to costs and oversight.
This distinction is not theoretical: in February 2026, ChatGPT had more than 900 million weekly users and more than 50 million consumer subscribers, according to OpenAI. They all use chat; how many also have tasks carried out depends on the features they have enabled. That distinction between answering and doing runs through this article.
Four points to keep in mind as you read on:
A chatbot answers questions; an AI agent plans steps and uses tools.
The label says little: ChatGPT also has agent features.
Compare costs per task, including setup and review.
Test one clearly defined task before you decide.
Agentic AI works toward a goal by planning steps, using tools, and checking results. IBM describes the distinction this way: generative AI creates new content, while "agentic AI focuses on achieving goals by planning, making decisions and carrying out multi-step workflows with varying levels of autonomy." Those capabilities can come together in one product. ChatGPT is one example: you can chat with it and, with the available tools or agent features, have it carry out work. Look at the enabled features and permissions, not just the interface.
A chatbot is a conversational interface. A basic question-and-answer chatbot without action tools provides answers but changes nothing in your systems. Other chat products can process files, execute code, or use connected services.
ChatGPT demonstrates this: it can analyze files and execute Python code, remember context and use apps to perform supported actions. For longer tasks, OpenAI offers ChatGPT Work. The features available to you depend on your subscription, settings, and granted access.
Not every chatbot works the same way. A rule-based chatbot follows a script: it recognizes set questions or buttons and replies with prepared answers. An AI chatbot uses a language model, so it can phrase answers freely and handle questions nobody scripted. Neither type changes anything in your systems unless someone connects it to tools.
An AI agent starts from a goal instead of a single question. It plans the steps, uses the tools and permissions you give it, and checks the result before it moves on. In practice, the lines blur: an AI chatbot with connected actions already behaves partly like an agent. That is why the table below compares capabilities, not product labels.
An AI agent such as Hermes Agent can read and edit files, run scripts, and check results when it has the right access. The agent can choose its own next steps within the task. That does not guarantee a correct result: agree on which actions need approval first and who reviews the work.
An agent that can act can also act wrongly. It might edit the wrong file, misread a source, or follow instructions hidden in a web page or document it reads. The OWASP Top 10 for LLM Applications lists prompt injection and excessive agency among the main risks. An agent with more functions, permissions, or autonomy than the task needs can do damage when something goes wrong.
You limit those risks with the same basics every time. Give the agent only the access the task needs, decide which actions require approval, and review the work afterward. For a full list of checks, see our practical safety checklist for AI and business data.
Suppose you need to update a web page. With a question-and-answer chatbot that has no writing tools, you ask for advice and apply the suggestion manually. An agent with access to your project or CMS can read the page, edit it, and check the result. A chat product can do that too, if the required tools and permissions are available.
This is how we work ourselves. Our own Hermes installation at Voltti reads our Search Console data and creates a prioritized list of articles that could use revision. Search Console access is read-only. The agent edits content directly in Storyblok and sometimes publishes it itself, after which we review the work and make corrections where needed. This article is one example: the agent identified it and developed it, and a person reviewed the result.
For more on what an agent can do, see our overview of AI agents for businesses.
This table compares a basic question-and-answer chatbot without action tools with an AI agent configured for a task. These are not fixed boundaries between brands: ChatGPT also has tools, memory, and agent features.
| Difference | Basic question-and-answer chatbot | AI agent with configured tools |
|---|---|---|
What it does | Answers in text | Plans and carries out steps; the result still needs review |
File access | Uses supplied text or a knowledge source; no writing actions | Reads and edits allowed files with appropriate permissions |
Memory | Depends on the setup; memory is not exclusive to agents | Can retain context when memory or storage is configured |
Using tools | No action tools in this comparison | Can use configured scripts, a browser, and integrations |
Planning steps independently | Responds to questions; you decide the next step | Can break down a task and choose subsequent steps |
Human review | Review answers before using them | Approval according to agreed rules; review completed work |
Where it works | Through the web, apps, or connected chat channels | Through chat or scheduling, with access to configured systems |
Compare the costs for the same task, not just the chatbot or agent label. A chat product may have a subscription or usage limit; extra model usage, tools, and multistep tasks can add costs in either product format. Include setup, support, and human review too. For help working this out for your situation, read our explanation of AI agent costs.
We cannot give a fixed amount without knowing your process. Voltti provides a custom quote after a pilot that establishes which tasks the agent will handle. How to define that pilot upfront is covered in preparing your AI agent pilot in seven questions. What you can always track is usage: in our own environment, we continuously see how much the agent uses, so costs are not a black box.
Choose based on the work and the access it requires. An agent may help with recurring tasks, but a chat product with scheduled tasks may also fit. Test the features you already have first.
| Situation | Best choice | Why |
|---|---|---|
You mainly want to explore ideas and ask questions | Chatbot | Quick and sufficient for text advice |
An agent or chat product with scheduled tasks | Scheduling, access, and any memory must be configured for the task | |
Customers keep asking the same questions and you want answers 24/7 | Chatbot | A bot without actions is enough for frequently asked questions; see what works for an AI agent for customer service |
You want tasks to be completed in your systems | An AI agent or chat product with suitable tools | The agent reads, edits, and checks, with approval where needed |
Your team is small and every manual task counts | Test an agent on one clearly defined task | Decide only after checking the result, required permissions, and total costs |
AI agent or chatbot? A basic chatbot may be enough for questions and text advice. If you also want changes to files or systems, choose a product with the right tools, permissions, and checks. That could be your own AI agent or a chat product with action features. Test it with one clearly defined task.
Want to see which tasks an agent could handle in your business? Explore our overview of AI agent use cases or first read what an AI agent is. Voltti helps businesses in Belgium and the Netherlands move from pilot to everyday use. You can also explore Hermes, ChatGPT, and Copilot to see how it fits the way you work. Thinking about an assistant of your own? Read about building or outsourcing your own AI assistant.
Sharing this with Dutch-speaking colleagues or clients? There is also a Dutch version: AI-agent of chatbot: de 7 verschillen die ertoe doen.
Yes, if it has connected tools or predefined actions. A basic question-and-answer bot without those connections does not make changes. An AI agent goes further by planning steps toward a goal and using the results to choose the next step. Check what each product has enabled and what it is allowed to do.
ChatGPT combines a chat interface with tools and agent features. It can analyze files, execute Python code, and perform supported actions through available apps. ChatGPT Work is intended for longer, multistep tasks. Whether you can use those features depends on your subscription, settings, and access permissions.
A rule-based chatbot follows predefined rules. An AI chatbot uses a language model to formulate answers. Both can connect to tools or predefined actions; the language model alone does not determine whether the system may change anything. An AI agent centers on planning and carrying out steps toward a goal.
Not necessarily. A well-designed AI agent works through the same chat interface as a chatbot. What changes is the setup: you agree on which systems the agent may use, which actions need approval, and who reviews the work. The conversation itself stays the same.
Yes. A chatbot on your website can answer common questions. An agent can then handle the follow-up, such as preparing a reply or updating a record, with approval where needed. One product can also do both. Choose per task, and agree who checks the result.
