A chatbot answers questions in a conversation. An AI agent works toward a goal by deciding which steps to take and using tools it has been given, such as reading a CRM record or drafting an email. An agent can change things in your systems, so it needs tighter controls: limited permissions, approval steps and logging.
Side by side
- Chatbot output: a message. Agent output: a changed record, a routed request, a prepared document.
- Chatbot risk: a wrong answer. Agent risk: a wrong action in a real system.
- Chatbot controls: content guidelines. Agent controls: permissions, approvals, logs, limits.
When a chatbot is enough
If users mainly need answers from known information, such as opening hours, policies or product details, a well-grounded chatbot is simpler and safer.
When an agent is worth it
When a task requires reading information, deciding a next step and updating systems, repeatedly and at volume. For example: triaging incoming requests, preparing CRM updates from emails, or assembling case summaries for staff.
Limitations and considerations
- Agents can be misled by content they read, so input must be treated as untrusted.
- Agents multiply the cost of errors; evaluation and monitoring are essential.