RAG vs AI Agents

RAG retrieves relevant information and gives it to a model for generation. An agent decides among actions, tools, and multiple steps. They solve different problems.

Use RAG when

• The main need is grounded question answering over documents.
• The workflow is retrieval → answer.
• You want predictable cost and latency.

Use an agent when

• The system must decide which actions to perform.
• Several tools may be needed in different orders.
• The path cannot be predetermined reliably.

Combine them

A retrieval tool can be one capability inside an agent. The agent decides when search is needed; RAG supplies grounded context.