Start with the task, not the tool.
Forwarding an invoice, explaining a policy and interpreting a conflicting order are different tasks. Each can be supported digitally. Not all require an AI agent.
The terms also describe different layers: a chatbot is primarily a conversational interface. Behind it may be fixed rules, a language model or an agent. What matters is what the system can actually do and how the workflow is controlled.
Rule-based automation: when the path is already defined.
When inputs, rules and outputs are unambiguous, a language model is often unnecessary. For example, a completed form can be validated, assigned to a topic and stored in the right system. The next step is defined for every allowed input.
The strength is traceability. Free text, missing details and conflicting documents require explicit handling. More rules can help, but maintaining them can eventually become more demanding than the original task.
Chatbot: when people need an answer.
A conversational interface is useful when people should ask questions in their own words. A system with knowledge access can find relevant approved documents and prepare an answer with a source. Its value depends on current sources that actually support the answer.
A fluent answer is not necessarily correct. Missing evidence or a personal exception should trigger a clear boundary and a handover. The interface alone says nothing about quality or permitted actions.
AI agent: when interpretation and next steps come together.
An AI agent can interpret information in context and use tools within defined boundaries. For an order, it might compare a message with order details, flag a different address and prepare a clarification. It does not need a separate fixed workflow for every wording.
This flexibility needs controls: permitted tools, limited access, assessable outputs and explicit handling of uncertainty. A consequential change should not happen simply because a model considers it plausible.
A combination is often the practical choice.
AI interprets variable information. Fixed logic checks required fields and permissions. People approve decisions requiring responsibility and experience. An agent does not need to own the entire process.
Before deciding, record which inputs vary, where expert judgement is needed and which steps can be checked unambiguously. Then test the smallest useful scope against the current workflow. Technical complexity is justified only by demonstrable value.