Agentic RAG: Beyond the Simple Vector Search

In 2025, we've realized that "Naive RAG" (just searching a vector DB and stuffing the results into a prompt) is too brittle for production. It suffers from hallucinations, irrelevant context, and an inability to say "I don't know." The solution is Agentic RAG, powered by tools like LangGraph.

From Chain to Graph

A traditional RAG pipeline is a linear chain. An Agentic RAG pipeline is a directed graph where nodes represent actions (retrieve, grade, rewrite, generate) and edges represent logic.

Self-RAG Implementation

One of the most popular patterns in 2025 is Self-RAG, where the agent evaluates the quality of the retrieved documents before using them.

from langgraph.graph import StateGraph, END

def retrieve(state):
    # Search vector DB
    documents = vector_db.similarity_search(state["question"])
    return {"documents": documents}

def grade_documents(state):
    # LLM decides if documents are actually relevant
    filtered_docs = [doc for doc in state["documents"] if llm_is_relevant(doc, state["question"])]
    if not filtered_docs:
        return "rewrite_query"
    return "generate"

workflow = StateGraph(AgentState)
workflow.add_node("retrieve", retrieve)
workflow.add_node("grade_documents", grade_documents)
workflow.add_node("generate", generate)
workflow.add_node("rewrite_query", rewrite_query)

workflow.set_entry_point("retrieve")
workflow.add_conditional_edges("grade_documents", grade_documents_logic)
workflow.add_edge("rewrite_query", "retrieve")
workflow.add_edge("generate", END)

Tool Calling as a First-Class Citizen

In 2025, the models themselves are much better at Tool Calling. Instead of parsing regex from an LLM response, we use native JSON schemas to let the agent decide which tool to use.

tools = [tavily_search, sql_query_tool, internal_wiki_search]
model = ChatOpenAI(model="gpt-4.5-preview").bind_tools(tools)

Why This is Better

1. Iterative Retrieval: If the first search doesn't find the answer, the agent can try a different query.
2. Verification: The agent can verify that the generated answer is actually supported by the documents.
3. Ambiguity Handling: The agent can ask the user for clarification if the question is too vague.

Agentic RAG turns AI from a "stochastic parrot" into a reasoned researcher.