What is Agentic RAG? (vs. Graph RAG and Vector RAG)

What is Agentic RAG? (vs. Graph RAG and Vector RAG)

Retrieval-Augmented Generation (RAG) is the foundational architecture of enterprise AI. It prevents models from hallucinating by forcing them to look up real company documents before answering a question.

But RAG is evolving rapidly. Search trends show engineering teams are deeply curious about the next phases of retrieval, constantly searching for terms like "what is agentic rag", "graph rag vs traditional rag", and "how agentic rag works".

In this post, we will define the three eras of RAG and explain why Agentic RAG is the ultimate goal for enterprise AI memory.

1. Traditional Vector RAG (The Search Engine)

Vector RAG is the first generation. It works by breaking documents down into "chunks," converting them into numbers (embeddings), and storing them in a vector database.

When you ask a question, it finds the text chunks that are mathematically most similar to your question.

• Best for: Simple Q&A ("What is our WFH policy?")
• The Problem: It is "blind" to relationships. If you ask a coding agent to debug a complex microservice issue, Vector RAG will pull 10 random code snippets that share the same keywords, missing the actual architectural flow entirely.

2. Graph RAG (The Relational Mapper)

Graph RAG solves the relationship problem by using Knowledge Graphs. Instead of just chunking text, it extracts entities and relationships (e.g., Developer A -> committed to -> Repo B -> which caused -> Incident C).

When a user asks a question, Graph RAG traverses this web of relationships.

• Best for: Complex reasoning, architectural debugging, and enterprise search where context matters.
• The Problem: While Graph RAG provides incredible context, it is still passive. The AI receives the context, answers the question, and stops.

3. Agentic RAG (The Autonomous Worker)

Agentic RAG is the bleeding edge of enterprise AI. It combines Graph RAG with AI Orchestration, Planning, and Tool Calling (like MCP Servers).

In Agentic RAG, the retrieval process is not a single database dip. It is a multi-step, dynamic research process driven by the AI agent itself.

How Agentic RAG Works:

1. Initial Query: The user asks, "Why did our AWS bill spike yesterday?"
2. Dynamic Planning: The agent realizes it needs multiple pieces of data.
3. Iterative Retrieval: It first queries the Graph RAG to find who deployed infrastructure yesterday. It finds a new ECS cluster was spun up.
4. Tool Execution: Using an MCP server, the agent actively queries the live AWS Cost Explorer API for that specific cluster.
5. Synthesis & Action: The agent synthesizes the retrieved data, writes a summary, and (with human approval) executes an MCP command to scale down the cluster.

Why Enterprises are Moving to Agentic RAG

Traditional RAG builds a highly advanced search bar. Agentic RAG builds a digital employee.

By giving AI agents the ability to reason about what data they need, iterative search capabilities, and the tools to take action, enterprises are achieving massive productivity gains in DevOps, Customer Support, and Product Management.

To transition your organization from basic vector search to powerful Agentic RAG workflows backed by Bitemporal Knowledge Graphs, explore Memora's AI Memory Platform.