Guides
AI agent or chatbot: what is the difference?
Every chat window on a website is called a chatbot, and more and more of them are now called AI agents. The difference is not the chat window but what happens behind it: fixed paths, answers from your documents, or an agent that looks things up in your systems and takes actions. This guide explains the three, as of October 2026.
Short answer
A chatbot answers: by fixed paths, or with generative AI from your documents. An AI agent also acts: it decides which steps to take, reads data from systems such as your ERP or CRM and can, for example, create a lead or a ticket. Choose a chatbot for general questions with stable answers; an agent when the answer depends on data in your systems or a follow-up action is needed.
What is the difference between a chatbot and an AI agent?
A chatbot holds a conversation; an agent completes a task. OpenAI describes agents as systems that independently accomplish tasks on your behalf and says simple chatbots are not agents. Anthropic distinguishes workflows, which follow predefined code paths, from agents, which direct their own process and choice of tools.
Google Cloud places bots at the least autonomous end, following pre-programmed rules, and agents at the other end, with reasoning, planning and memory. Microsoft describes an agent in Copilot Studio as an assistant that follows your instructions, uses connected knowledge and uses tools to act, deciding the best next step itself.
Sources
- OpenAI: A practical guide to building agents (cdn.openai.com)
- Anthropic: Building effective agents (anthropic.com)
- Google Cloud: What are AI agents? (cloud.google.com)
- Microsoft Learn: what is Copilot Studio (learn.microsoft.com)
Rule-based chatbot, generative chatbot or AI agent?
Three levels. A rule-based chatbot recognises the question and follows a path someone designed. A generative chatbot writes its own answer from your documents or website. An AI agent also uses tools: it looks things up in your systems and can take an action, such as creating a lead.
Rule-based does not mean keyword-only. In Microsoft's classic orchestration and in Google Dialogflow, the input does not have to match a trigger or training phrase exactly; the platform recognises similar phrasings.
| Rule-based chatbot | Generative chatbot | AI agent | |
|---|---|---|---|
| How it answers | Recognises the question and follows a designed path | Writes an answer from your documents or website | Decides the next step itself and uses tools |
| Data from your systems | Only where a path calls a fixed integration | Usually not; works from documents | Reads ERP, CRM or service desk through the API |
| Actions | Fixed steps in the path | None, or very limited | For example creating a lead, a ticket or a draft order |
| Strength | Predictable | Answers questions nobody scripted | Completes a task across systems |
| Watch out for | Questions outside the paths | Answers that sound right but are wrong | Rights and approval of actions |
Sources
- Microsoft Learn: generative and classic orchestration (learn.microsoft.com)
- Microsoft Learn: topics in Copilot Studio (learn.microsoft.com)
- Google Cloud: Dialogflow ES intents (cloud.google.com)
When is a chatbot enough?
When the questions are general and the answers stable: opening hours, delivery terms, product information that is on your website. Anthropic notes that for many applications a single well-prepared model call with retrieval is enough, and OpenAI that a deterministic solution may suffice where rules work.
- Most questions can be answered from your website, manuals or FAQ.
- No answer depends on who the customer is or on live data such as stock or order status.
- Nothing has to happen in another system after the conversation.
When do you need an AI agent?
When the answer depends on data in your systems or a follow-up action is needed. A dealer asks whether 40 pieces can ship this week, a customer asks about their order, a visitor wants a quote: the agent looks it up in your ERP or CRM, asks what is missing and prepares the next step for your team.
| Question | Chatbot without system integration | AI agent |
|---|---|---|
| What are your delivery terms? | Answers from the website | Answers from the website |
| Where is my order 10432? | Refers to customer service | Verifies the customer, reads the order status in the ERP |
| Which pump fits my installation? | General product information | Asks for application and volume, advises and creates a qualified lead in the CRM |
| My machine has a fault | Gives the service phone number | Asks for serial number and photo, creates a complete ticket |
What are the risks of an AI agent?
An agent can do more, so it can also do more wrong. OWASP names excessive agency: too many functions, rights or autonomy, so that a wrong or manipulated output leads to a harmful action. Prompt injection, where text from a user or a file changes the agent's behaviour, adds to that risk.
- Limit rights: OWASP advises minimal permissions and checking authorisation in the connected system, not in the model.
- Human approval for actions with impact: OpenAI names cancelling orders, large refunds and payments as examples that need human oversight until there is enough confidence in its reliability.
- Grounding in your own data reduces wrong answers but does not remove them. Microsoft says generated answers can contain mistakes.
Sources
- OWASP: LLM06 Excessive Agency (genai.owasp.org)
- OWASP: LLM01 Prompt Injection (genai.owasp.org)
- Microsoft Learn: generative answers FAQ (learn.microsoft.com)
Does the AI Act treat chatbots and agents differently?
Both fall under the same rule since 2 August 2026: when they interact with people, those people must be told they are dealing with AI, unless that is obvious. The Commission's guidelines name customer support chatbots and AI agents that make bookings, handle correspondence or execute purchases. According to the Commission, an agent should also say on whose behalf it acts.
Sources
- European Commission: guidelines on transparency obligations (Article 50) (ai-act-service-desk.ec.europa.eu)
What does a BeeManaged agent do?
It is an agent: it holds the conversation, reads your systems through their API and prepares the next step. It works in the systems you already use, gets only the rights its task needs and is read-only by default. Actions go through an approval queue by default, and we read conversations, tune the agent and report monthly.
- Channels: website, WhatsApp and email.
- Hands over to your team when a person is needed, with the conversation attached.
- Works in several languages; for product advice it has a decision tree as backup.
Updated · As of October 2026 · Author Roy Rutten, AI architecture and development
Frequently asked questions
What is the difference between a chatbot and an AI agent?
A chatbot answers questions, by fixed paths or with generative AI. An AI agent also uses tools: it reads data from your systems and can take an action, such as creating a lead or a ticket.
Is a ChatGPT-style chatbot on my website an AI agent?
Not by itself. If it only answers from your documents, it is a generative chatbot. It becomes an agent when it uses tools to look things up in systems or take actions.
Is a chatbot enough for my company?
Yes, if most questions are general and the answers are on your website. If answers depend on order status, stock or the customer's own data, you need an agent.
Can an AI agent make mistakes?
Yes. Grounding in your own data reduces wrong answers but does not remove them. That is why an agent needs limited rights, human approval for actions with impact and monitoring.
Must a chatbot or agent say it is AI?
Yes, since 2 August 2026 under Article 50 of the AI Act, unless that is obvious. According to the Commission, that exception does not apply to chatbots on a website or helpdesk whose replies people may take for a human's.
What does a BeeManaged agent cost?
Customer-facing agents cost from €0.35 per conversation; setup is quoted up front and monitoring with the dashboard costs €250 per month. Internal agents are custom work, priced in consultation.
Chatbot or agent: which fits your questions?
Plan a conversation. We look at the questions your customers and team ask, and tell you whether a chatbot is enough or an agent pays off.