AI systems · definitions
AI chatbot vs AI agent
Most teams say “agent” when they mean “chat interface with a prompt.” The distinction matters because the engineering, risk, and budget profile changes the moment the system starts taking actions instead of only producing text.
The short answer
What matters most.
A chatbot answers. An agent acts. If the system reads data, calls tools, updates state, or triggers workflows, you are in agent territory whether or not the UI looks like a chat window.
Breakdown
Core difference
Chatbots generate responses inside a conversation. Agents generate responses and also do work outside the conversation: fetching data, writing records, calling APIs, or coordinating steps.
Risk difference
A chatbot can be annoying. An agent can break something. That means agents need tighter permissions, stronger review paths, and much better logging.
Where teams get confused
The front-end can be identical. A support widget can look like a chatbot while actually being an agent under the hood because it classifies, routes, and updates systems after the user message lands.
When to use which
Use a chatbot when the job is mostly answering. Use an agent when the job includes decisions, routing, or actions that save human time.
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