Agentic RAG Explained in Simple Words

What is RAG in the first place?

Before “agentic RAG”, there is RAG: Retrieval‑Augmented Generation.​

• You ask a question.

• The system retrieves some documents from a knowledge source.

• Then an LLM generates an answer using those documents as context.​

In a basic (vanilla) RAG system, this usually means: one vector database + one LLM + one round of retrieval.​

What is an AI agent?

An AI agent is an LLM that can:

• Follow a role and task (for example, “helpful coding assistant”).

• Use memory (short‑term conversation and long‑term facts).

• Do planning (decide next steps, reflect, route queries).

• Call tools (web search, calculator, email API, vector DB, etc.).​

The ReAct pattern (Reason + Act) is a common way to build agents:

• Thought: The agent reasons about what to do next.

• Action: It calls a tool.

• Observation: It looks at the result and decides the next step.
  This loop repeats until the task is done.​

So what is Agentic RAG?

Agentic RAG = RAG + agents.
Instead of a fixed, one‑shot pipeline, an agent controls retrieval and tools.​

The agent can:

• Decide whether to retrieve anything at all.

• Decide which tool to use (vector DB, web search, API, calculator, etc.).

• Rewrite or decompose the query.

• Check the retrieved context and decide to try again if it’s not good enough.​

In short:

• Vanilla RAG is like a simple “search once, then answer” system.

• Agentic RAG is like a smart assistant that plans, searches multiple places, checks its work, and only then answers.​

Agentic RAG architecture

There are two main patterns.

1. Single‑agent RAG

Here you have:

• One agent.

• Many knowledge sources and tools (vector DBs, web search, Slack API, email API, etc.).​

The agent’s job is to route each query:

• “For this question, I should search the internal vector DB.”

• “Now I also need a web search.”

• “Now I should call this API.”

This already solves a big problem of vanilla RAG: you are no longer stuck with just one external source.​

2. Multi‑agent RAG systems

Here you use multiple agents, each with a special job. For example:​

A master agent that coordinates everything.

A docs agent for company PDFs and internal knowledge.

A personal agent for your emails and chat history.

A web agent for public web search.

The master agent decides:

• Which specialist agent to call.

• How to combine their results into a final answer.

This makes the system more powerful and flexible, especially for complex, multi‑step tasks.​

Agentic RAG vs vanilla RAG

Feature	Vanilla RAG	Agentic RAG
Uses external tools (APIs, web)	No	Yes​
Works with multiple data sources	Limited / single source	Yes, agent routes between sources​
Query pre‑processing / rewriting	No	Yes (agent thinks before searching)​
Multi‑step retrieval	No (one shot)	Yes (can loop, refine, retry)​
Validates retrieved context	No	Yes (reasoning and checks)​

A good mental image from the article:

• Vanilla RAG = sitting in a library with books only.​

• Agentic RAG = having a smartphone: web, email, calculator, APIs, plus the library.​

How do you build Agentic RAG

There are two main ways.

1. Use LLMs with function calling

• You define functions/tools (for example, search in Weaviate, call an API).​

• You pass a tool schema to the model (describe the function name, parameters, description).

• The model decides when and how to call the tools.

• You write a loop that:

  • Sends user input + tools to the model.

  • Checks if the model wants to call a tool.

  • Executes the tool, sends results back to the model.

  • Repeats until the model returns a final answer.​

This gives you fine control at the API level.

2. Use an Agent Framework

Frameworks make this easier by providing building blocks for agents and tools.​

Examples from the article:

• DSPy – ReAct agents, automatic prompt optimization.

• LangChain / LangGraph – tooling, graphs of agents and tools.

• LlamaIndex – strong retrieval tools (QueryEngineTool, etc.).

• CrewAI – multi‑agent collaboration, shared tools.

• Swarm – OpenAI framework for multi‑agent orchestration.

• Letta – focuses on agent memory and world models.​

These frameworks help you quickly plug together: LLM, tools, memory, and retrieval into an Agentic RAG pipeline.

Why enterprises are moving to Agentic RAG

Enterprises are moving beyond vanilla RAG because they need systems that:

• Handle complex, multi‑step queries.

• Pull from many data sources (internal docs, tickets, code, web).

• Work more autonomously, not just “search once and answer”.​

Benefits from the article:

• Better retrieval quality (routed to the right source, with better queries).

• Validation of retrieved context before answering.

• More robust and accurate responses, closer to how humans research.​

Examples mentioned include development assistants (like Replit’s agent) and Microsoft’s copilots working alongside users.​

Limitations and trade‑offs

Agentic RAG is not magic; it has costs.​

• More latency: Each extra tool call or reasoning step adds time.

• LLM weaknesses: If the model reasons poorly, the agent may get stuck or fail the task.

• Complexity: More moving parts (agents, tools, memory, routing) means more to design, debug, and monitor.​

So production systems need:

• Clear failure modes.

• Good evaluation and logging to see when the agent helps or hurts.