These three terms get used interchangeably, but they solve different problems. A chatbot is a conversational interface; RAG (Retrieval-Augmented Generation) grounds answers in your own data so they're accurate; an agent uses tools to take actions on your behalf. Most businesses need RAG first — accurate answers from their data — and an agent only where taking actions adds real value.
The three, defined
- Chatbot: the conversational layer users talk to. On its own, it will happily make things up.
- RAG: retrieves relevant chunks of your data via vector search and passes them to the model, so answers are grounded and traceable.
- Agent: given tools (search, APIs, actions), it plans and executes steps — booking, updating records, running workflows.
Which do you need?
- Answering questions from your docs/help content accurately: RAG (usually behind a chatbot UI).
- Just a scripted conversational flow: a chatbot may be enough.
- Taking actions across systems (create, update, trigger): an agent — but only with guardrails.
How they combine
Real systems layer them: a chat interface on top, RAG for grounded answers, and agent tools for the few places actions genuinely help. The mistake is building a complex autonomous agent when a grounded chatbot would have solved the problem more reliably and cheaply.
Our approach
We start with the simplest thing that works — usually grounded RAG — and add agentic tools only where they earn their keep, always with guardrails and an evaluation set. That keeps the system accurate, debuggable, and cost-controlled.