Agentic RAG: Embedding RAG Within Deliberative AI Agents

TL;DR

Agentic RAG embeds RAG within a deliberative AI agent that plans its own retrieval steps. Use Cases: Dynamic, multi-step tasks like research assistants, coding helpers that fetch libraries/docs, planning tools, or any workflow where the query evolves. The agent iteratively refines its query, self-corrects, and uses external tools before finalizing the answer.

What Is Agentic RAG?

Agentic RAG refers to adding AI agents that control retrieval. The LLM acts as a planner that decides when and what to retrieve, rather than having a fixed retrieval pipeline. It maintains memory of past answers and decides when to call retrievers or tools.

When to Use Agentic RAG

• Dynamic, multi-step research tasks where the query evolves.
• Coding assistants that fetch libraries and documentation on demand.
• Planning tools and multi-turn customer support scenarios.
• Any workflow where the AI needs to reason about what information is needed next.

Building Agentic RAG in N8N

• Initialize the workflow with a prompt asking the LLM what information it needs.
• Use a Loop node to repeat the process.
• The LLM decides: Should I retrieve? Which tool should I use?
• Execute the appropriate API call or database query.
• Feed results back to the LLM with the question: What information do I need next?
• Use If/Switch nodes to route based on LLM reasoning.
• Continue until the LLM determines it has sufficient information to answer.

Implementation Patterns

• ReAct (Reasoning + Acting): Agent thinks through steps, then acts.
• Chain-of-thought reasoning: Instruct system to retrieve via explicit reasoning.
• Tool-use agents: Maintain a toolkit and let the agent decide which to call.
• Memory-augmented agents: Track past queries and answers to avoid redundant retrievals.

Strengths & Weaknesses

Strengths: Highly flexible and autonomous, can iteratively refine queries, handles complex workflows by reasoning, can correct itself and adjust strategy. Weaknesses: Hard to control and debug, performance depends on agent reasoning, may loop indefinitely or go off-track, slower and costlier due to multiple steps, safety and sandboxing concerns.

Metrics to Track

• Success rate on goals — did the agent answer correctly?
• Number of steps to solution — is it efficient?
• Time and cost per query — what's the overhead?
• Loop count — does it get stuck looping?
• Tool usage patterns — which retrievers/APIs does it favor?