One‑page guide to the three RAG architectures: Classic, Graph, and Agentic

Many teams that integrate large language models (LLMs) into business systems first ask whether the AI can answer questions from internal documents such as employee handbooks, product manuals, or knowledge bases. The difficulty is that a vanilla LLM only knows what is stored in its parameters and cannot access private, up‑to‑date company data, often producing fluent but unfounded answers. Retrieval‑Augmented Generation (RAG) solves this by first retrieving relevant material and then prompting the LLM with that context.

Core differences of the three RAG architectures

Classic RAG: retrieve

Classic RAG follows a straightforward pipeline: the user query is embedded, the vector database is searched for the top‑K most similar text chunks, and the query together with those chunks is sent to the LLM for answer generation. The seven steps are:

Split documents into chunks.

Convert each chunk into an embedding.

Store embeddings in a vector store.

Embed the user question at query time.

Retrieve the top‑K similar chunks.

Combine the question and retrieved chunks.

Generate the final answer with the LLM.

Example: an employee asks, “When does the parental‑leave application deadline end?” Classic RAG looks up HR policies, employee handbooks, and other internal documents, returns the most relevant paragraph, and the LLM produces a natural‑language answer. Classic RAG excels at FAQ‑style, policy‑lookup, product‑manual queries because the answer usually resides in a single document fragment. Its advantages are simplicity, low cost, fast latency, and mature tooling. The limitation is that it only finds “similar text”; it cannot follow multi‑hop relationships such as “Who is A’s manager’s manager?” or chain information across multiple documents.

Graph RAG: connect

Graph RAG builds on Classic RAG by adding a knowledge‑graph layer that captures entities (person, product, part, team, department, supplier, customer, system module) and their relationships (e.g., “A reports to B”, “Product X uses part Y”). After the usual chunking and embedding, an additional extraction step creates the graph.

The dual pipeline is:

Chunk and embed text as in Classic RAG.

Extract entities and relations from the text to construct a knowledge graph.

At query time the system can both retrieve relevant text and traverse the graph to follow relationship paths. Example: to answer “Which products are affected by a semiconductor shortage?” the system links product → circuit board → chip → supplier, something Classic RAG would miss. Graph RAG is therefore suited for impact analysis, dependency analysis, organizational‑relationship queries, and supply‑chain impact studies. Its main costs are graph construction and maintenance; inaccurate entity or relation extraction degrades results, and the graph must be continuously updated as the business evolves. If a domain is not modeled in the graph, the system cannot answer the query.

Agentic RAG: reason

Agentic RAG introduces an autonomous agent that decides the next investigative step, selects tools, and iterates until sufficient evidence is gathered. Instead of a single retrieve‑then‑answer pass, the agent may perform multiple retrievals, tool calls, and re‑planning.

Illustrative workflow for analyzing a sales‑decline problem:

Decompose the problem (product, region, time period).

Query the sales database for trend verification.

Check price history for adjustments.

Inspect marketing activity logs.

Review customer‑service tickets for complaints.

Examine inventory for stock‑outs.

If evidence is insufficient, choose additional data sources.

Combine all evidence and present hypotheses with supporting data.

This dynamic, multi‑step approach makes Agentic RAG ideal for complex, open‑ended investigations such as root‑cause analysis, cross‑system audits, technical troubleshooting, competitor analysis, and investment research. The trade‑offs are higher latency, greater token consumption (each planning and tool call costs tokens), and more difficult debugging because failures can stem from tool selection, insufficient evidence, or planning errors.

How to choose the right RAG architecture

Use the shape of the business question rather than the architecture name:

If the answer can be found in a single document fragment (e.g., policy dates, product configuration), start with Classic RAG.

If the answer depends on traversing relationships across entities (e.g., impact analysis, dependency mapping), adopt Graph RAG.

If the query path is unknown and requires multi‑step investigation (e.g., why sales dropped, multi‑system fault diagnosis), employ Agentic RAG.

A pragmatic incremental strategy is recommended: first deploy Classic RAG to cover the majority of straightforward queries, then add Graph RAG for relationship‑heavy use cases, and finally introduce Agentic RAG for the few open‑ended, multi‑step problems. This follows the engineering principle of solving 80 % of cases with the simplest solution before adding complexity.