AI agent vs chatbot: which does your business actually need?

A chatbot is a conversational interface. It might follow a decision tree, retrieve approved answers or use a language model to draft a response. An agent typically uses tools to pursue a task, such as finding an order and preparing a change. Product labels are inconsistent, so ask what the system actually does instead of assuming that 'agent' means a specific level of capability.

If visitors repeatedly ask about opening hours, delivery regions or service scope, an information assistant may be enough. If a task requires reading several systems and proposing the next action, an agent may be useful. If all inputs and branches are predictable, a conventional workflow can be simpler and easier to test than either option.

Separate answering, recommending and executing. Answering provides information from a controlled source. Recommending adds judgement, for example suggesting which team should handle an enquiry. Executing changes something in the business: creating a ticket, updating a record or sending a message. Each level introduces a different verification requirement.

A single interface can combine these levels without granting unrestricted access. The assistant can answer product questions automatically, draft a cancellation request and leave the actual cancellation to an authorised person. Design the permission boundary for each action individually. A good conversation experience does not require every underlying operation to be autonomous.

Imagine a retailer whose customers ask where their orders are. A public FAQ assistant can explain the usual delivery process but should not expose personal order data. After authentication, an order lookup can return the relevant status. If the customer requests an address change, the system must check whether the order is still editable before offering that action.

This is a hypothetical design, not a reported client implementation. Its value is showing where the components differ: retrieval answers the policy question, an integration fetches the order and deterministic rules decide whether a change is permitted. The model can interpret the request, but it should not invent the policy or bypass the checks.

The language-model bill is only one cost. Include source preparation, integrations, evaluation, human review and maintenance when estimating effort. An agent that edits several systems creates more operational responsibility than one that summarises a document. Compare the cost of a wrong action with the benefit of completing it faster.

Ask who will update the knowledge base when policies change and who will inspect failed tasks. If neither responsibility has an owner, keep the first release narrow. A smaller assistant with reliable answers is more useful than an ambitious agent whose team cannot explain what it has done.

Write a small set of representative customer requests and describe the correct outcome for each. Include requests the system should refuse and cases it should hand to a person. That document becomes a better acceptance test than a broad instruction to build an intelligent assistant.

For a first discussion about a custom agent, bring the workflow, approved information sources and a list of actions it may take. I can then help separate the conversational layer from the automation and decide which decisions need your team's approval.