A chatbot manages a conversation. An AI agent manages a task. Both may use the same language model, but an agent has tools, permissions, memory, and a workflow that lets it act outside the chat window. That creates more value,and more operational responsibility.
What chatbots do well
Chatbots are effective for FAQs, knowledge-base search, lead capture, navigation, and ticket creation. When the goal is to explain or route, a chatbot is simpler, cheaper, and easier to control.
What makes a system an agent
An agent receives a goal and uses tools to complete it: checking a CRM record, validating stock, proposing calendar slots, creating an order, and sending confirmation. It needs explicit rules for what it may do, what requires approval, and what happens when information is missing.
A customer-support example
A chatbot can explain the refund policy. An agent can identify the order, verify eligibility, request approval for an unusual amount, update the payment system, and notify the customer. Once the workflow ends in an external action, it has become agentic.
Do not begin with full autonomy
Start with recommendations, require human approval, then automate only after accuracy and edge cases are measured. Audit logs, least-privilege permissions, spending limits, and escalation paths are core product requirements,not security tasks added at the end.
A simple selection rule
If the system needs to answer and route, use a chatbot. If it must complete a repeatable workflow across systems, consider an agent. If the decision is sensitive, rare, or difficult to verify, keep a human in the loop.
Measure outcomes, not conversations
Track full resolution rate, handling time, errors, escalations, cost per completed task, and customer satisfaction. An agent that closes conversations quickly but creates manual repair work is not successful automation.