What a chatbot does
A chatbot holds a conversation. Modern chatbots built on large language models can answer questions well, especially when they are grounded in your own documents. But the conversation is the product: the chatbot replies, and a person still has to do whatever the answer implies.
That is often enough. A chatbot that answers product questions, explains a policy or points customers to the right page saves your team real time.
What an AI agent does
An AI agent is given a goal rather than a single question. It plans the steps, calls tools connected to your systems — the order database, the CRM, the accounting package — checks the results and keeps going until the task is done. When an action carries risk, such as issuing a refund, it pauses for a person to approve.
- Reads and updates systems, not just text
- Runs multi-step work in sequence
- Verifies results before finishing
- Escalates to a human for risky or unclear cases
- Leaves a trace of every step for review
A simple example
A customer writes: "My order arrived damaged." A chatbot can explain the returns policy. An agent can find the order, check the policy, prepare the refund, wait for approval if the amount is above a limit, send it, reply to the customer and close the ticket.
Which one do you need?
- Choose a chatbot when the value is in giving accurate answers and a person can easily act on them.
- Choose an agent when the same follow-up actions happen again and again and they involve your systems.
- Start with one narrow, frequent task either way, and measure the result before expanding.
Is an agent riskier?
It can be, if built carelessly. Well-built agents use least-privilege credentials, validate every action, require approval for anything that moves money or deletes data, and log everything. Those controls are part of the design, not an add-on.