The terms "AI Chatbot" and "AI Agent" are often used interchangeably, but technologically and functionally, they represent two entirely different paradigms in enterprise AI.
The AI Chatbot (Reactive)
An AI chatbot is fundamentally a conversational interface. Its job is to ingest a user's prompt, process it (often using RAG to fetch context), and return a text response.
Key characteristics:
- Reactive: It only acts when spoken to.
- Stateless Actions: It generally cannot execute complex, multi-step actions in external systems.
- Use Case: Customer support FAQs, internal knowledge retrieval, semantic search.
The AI Agent (Proactive & Autonomous)
An AI agent is a system where the LLM acts as the reasoning engine to drive external actions. Given a goal, an agent will create a plan, use tools (APIs, calculators, web browsers), observe the results, and iterate until the goal is met.
Key characteristics:
- Autonomous: It can run in the background (e.g., monitoring an inbox and acting on triggers).
- Tool Use: Agents interact with Salesforce, Stripe, AWS, and other APIs via Model Context Protocol (MCP) or direct function calling.
- Use Case: Autonomous lead research, end-to-end ticket resolution, automated code reviews.
Summary
If you want a system to answer questions based on a manual, build a Chatbot. If you want a system to log into a portal, download a report, summarize it, and email it to the team every morning—you need an AI Agent.