RAG vs Agent Architecture Comparison

What this template is for

RAG and AI agents solve different problems with overlapping technology. This comparison diagram lays the two architectures side by side: on the left, a RAG pipeline — the user's question is embedded, a retriever pulls relevant chunks from a vector database, and the LLM generates an answer grounded in those chunks; on the right, an AI agent — the LLM reasons about the question, consults memory, picks a tool, takes an action, and loops back with the observation. RAG is read-only and one-shot; an agent is multi-step and can act on the world. Use this template to choose between them, document a hybrid system, or explain the difference to a stakeholder before writing code.

When to use this template

• Decide whether your use case needs RAG, an agent, or both.
• Explain to a stakeholder why an agent isn't a drop-in upgrade for RAG.
• Document a hybrid system where an agent uses RAG as one of its tools.
• Plan the migration from a RAG prototype to an agentic version.
• Compare cost and latency profiles by pointing to extra loop iterations on the agent side.
• Show where retrieval ends and reasoning/acting begins.

How to use it

1. 1Draw two side-by-side frames — one for RAG, one for Agent.
2. 2On the RAG side, draw the linear flow: user → retriever → vector DB → LLM → answer.
3. 3On the Agent side, draw the loop: user → agent → memory + tools → action.
4. 4Show the feedback arrow from tools back to the agent — this is the key difference.
5. 5Label the RAG flow as one-shot and the agent flow as iterative.
6. 6Add notes on the tradeoffs: RAG is cheaper and predictable; agents are flexible but harder to debug.

Quick example