A chatbot holds a conversation and answers questions. An AI agent works toward a goal: it plans steps, calls tools such as your CRM or ticketing system, checks the results and decides what to do next. Choose a chatbot when people mainly need answers; choose an agent when the work involves several steps across systems. Many good products start as a chatbot and add agent steps where they are safe.
Key takeaways
- The difference is action, not intelligence: adding a large language model to a chatbot doesn’t make it an agent.
- Agents carry more risk because they change things. Every action needs a permission, a log and, for some actions, a person’s approval.
- A predictable, scripted sequence of steps (a workflow) is often better than a fully autonomous agent.
- Start with the simplest design that does the job and add autonomy only where it pays off.
What a chatbot does
A chatbot takes a message and returns a reply. Older chatbots followed scripts and decision trees. Modern ones use a large language model and, usually, retrieval from your own content, so they can answer questions in natural language and point to sources. A chatbot may look up information, such as an order status, but it doesn’t decide on its own to take a chain of actions.
Typical jobs: answering product and policy questions, helping customers find information, collecting details before handing over to a person.
What an AI agent does
An agent is given a goal and a set of tools. It decides which tool to call, reads the result, and repeats until the goal is met or it needs help. In its December 2024 guide Building effective agents, Anthropic separates workflows, where developers fix the sequence of steps in code, from agents, where the model decides how to proceed and which tools to use. Both are useful; the guide recommends finding the simplest solution that works and adding complexity only when needed.
Typical jobs: triaging an inbox and drafting replies with the right records attached, reconciling records across two systems, researching a lead and booking a call, resetting access after checking a policy.
AI agent vs chatbot at a glance
| Chatbot | AI agent | |
|---|---|---|
| Main job | Answer and guide | Complete a task |
| Uses tools | Sometimes, to look things up | Always, to look up and to act |
| Who decides the steps | The script or the user | The model, within limits you set |
| Risk if wrong | A wrong answer | A wrong action in a real system |
| Controls needed | Sources, handoff to a person | Permissions, approvals, step logs, a stop switch |
| Testing | Answer quality on real questions | Task success, tool use and safe stopping on past cases |
| Typical first project | Support or internal knowledge assistant | One back-office task with clear rules |
How to tell which one you need
Ask four questions about the job:
- Does finishing the job change something in a system? If yes, you need at least agent-like steps.
- Is the sequence of steps the same every time? If yes, a workflow with AI steps is safer and easier to test than an open-ended agent.
- What happens if it gets one step wrong? The higher the cost, the more steps need a person’s approval.
- Can you test it on past cases? If you can’t collect real examples with known good outcomes, start with a chatbot or a narrow workflow and gather them first.
A worked example
Example, for illustration. A logistics company receives hundreds of "where is my shipment?" emails a day.
- Chatbot version: a web and WhatsApp assistant answers tracking questions from the carrier data and hands complex cases to staff. It reduces the emails people send, but the inbox still needs sorting.
- Workflow version: each email is read, the shipment number extracted, the status fetched and a reply drafted for a person to send. The steps are fixed, so it is easy to test.
- Agent version: for delayed shipments, the agent also checks the carrier portal, opens a claim if the delay passes a threshold, and updates the customer record, with a person approving any claim before it is filed.
Most teams should ship the workflow first, measure it on past emails, then let the agent handle the cases where the steps vary.
Controls every agent needs
- A written list of allowed actions, and the ones that need approval: anything that spends money, contacts a customer or deletes data.
- Least-privilege access: the agent’s credentials can do only what its list allows. OWASP’s Top 10 for LLM applications (2025) lists excessive agency among the main risks for exactly this reason.
- Step-by-step logs of every tool call, input and output, so a person can audit what happened.
- Evaluation on real past cases before launch, including cases where the right move is to stop and ask (how to evaluate an LLM application).
- A way to switch it off that doesn’t depend on the agent.
How KaliIO approaches it
We start from the job, not the technology: map the steps, the systems and the decisions, then pick the simplest design that does it, which is often a chatbot or a fixed workflow with AI steps. Where an agent earns its place, we write down its limits before building it. See AI agent development, AI chatbots and voice assistants and intelligent automation for how each is built and tested.
Frequently asked questions
Is ChatGPT a chatbot or an agent?
Used for conversation, it behaves as a chatbot. When it is given tools, such as web browsing or the ability to run code, and decides on its own when to use them, it is acting as an agent. The label depends on what the system is allowed to do.
Are AI agents safe to use with customer data?
They can be, with the right controls: access limited to what the task needs, approval steps for sensitive actions, logs, and data handling that meets the rules you work under, such as GDPR or India’s DPDP Act. The risk comes from giving an agent more access than its task needs.
Do agents replace staff?
In practice they take over repetitive steps and prepare work for people to check. The person still owns decisions that carry risk, and handles the cases the agent hands back.
Can a chatbot become an agent later?
Yes. A common path is to launch a chatbot that answers questions, then add tool calls one at a time, starting with read-only lookups and moving to actions with approval.
Sources
- Anthropic (2024). Building effective agents.
- OWASP Gen AI Security Project (2025). OWASP Top 10 for LLM Applications 2025.