Agentic RAG Architecture & Self-Correction Pipelines

By AI Agent Hub Editorial Desk · Review method · Corrections

Traditional naive RAG (Retrieval-Augmented Generation) follows a rigid linear pipeline: Query → Vector Search → Prompt Concatenation → LLM Response. When vector retrieval returns irrelevant chunks or outdated documents, naive RAG fails completely with hallucinated answers. Agentic RAG transforms retrieval into a dynamic, multi-turn reasoning loop capable of self-grading, query rewriting, and fallback web search.

1. The Evolution: Naive RAG vs. Agentic RAG

2. Architectural Deep-Dive: Self-RAG State Machine

Agentic RAG introduces three crucial node evaluations in the graph execution:

1. Retrieve Chunks via Hybrid Search (BM25 + Dense Vectors)
       │
       ▼
2. Grade Document Relevance (Is chunk relevant to user question?)
       ├── [YES] ──► Generate Answer Draft
       └── [NO]  ──► Rewrite Query ──► Fallback Web Search Tool
                                              │
3. Check Hallucination (Is answer grounded in facts?) ◄┘
       ├── [YES] ──► Final Output
       └── [NO]  ──► Regenerate with strict constraints
      

3. Clean Python LangGraph Implementation

Below is a production-ready Python example using LangGraph and Cohere Cross-Encoder reranking:

from typing import TypedDict, List
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    question: str
    documents: List[str]
    generation: str
    needs_search: bool

def retrieve_node(state: AgentState):
    print("--- RETRACTING DOCUMENTS ---")
    # Vector DB query logic here
    return {"documents": ["Doc chunk 1...", "Doc chunk 2..."]}

def grade_documents_node(state: AgentState):
    print("--- GRADING RELEVANCE ---")
    # LLM relevance check logic
    relevant = True
    return {"needs_search": not relevant}

# Build LangGraph workflow
workflow = StateGraph(AgentState)
workflow.add_node("retrieve", retrieve_node)
workflow.add_node("grade", grade_documents_node)

workflow.set_entry_point("retrieve")
workflow.add_edge("retrieve", "grade")
app = workflow.compile()

4. Decide Whether Agentic Retrieval Is Justified

A routing loop adds latency, cost, and new failure modes. Start with a conventional retrieval baseline: one well-formed query, access-controlled search, a small set of reranked passages, and an answer that cites them. Add an agent only when measured failures require conditional behavior—for example, decomposing a multi-part question, switching between code and policy indexes, retrying a query after low recall, or refusing when no authoritative evidence exists.

5. Production State Machine

Represent each transition explicitly: classify, retrieve, grade_evidence, rewrite_query, answer, and stop. Store the query version, retrieved document IDs, scores, authorization decision, model response, and stop reason. Set hard limits on rewrites, documents, tokens, wall time, and tool calls. A graph that has no terminal state is a retry storm waiting to happen.

START -> authorize -> retrieve -> grade
                           | sufficient -> answer -> cite -> END
                           | weak and retries left -> rewrite -> retrieve
                           | missing/forbidden -> abstain -> END

Keep authorization outside the language model. Filters for tenant, project, region, and document permissions must execute before content enters the prompt. Likewise, the answer should cite stable record identifiers rather than model-generated URLs.

6. Evaluation That Finds the Broken Component

Create a frozen set containing ordinary questions, recently changed documents, missing-answer cases, conflicting sources, permission-restricted records, and indirect prompt injection. Measure corpus coverage, retrieval recall at the chosen context limit, citation correctness, supported-answer accuracy, abstention precision, p50/p95 latency, and cost per accepted answer. Attribute each failure to ingestion, retrieval, generation, policy, or orchestration so a team does not redesign the graph to compensate for a stale index.

Compare the agentic version with the simple baseline on exactly the same cases. Promote the loop only when it improves an agreed outcome enough to justify its extra calls and operational complexity.

7. Primary References

• LangGraph custom RAG agent tutorial
• Self-RAG paper
• AI Agent Hub: Fine-Tuning vs RAG decision framework

8. Related Architecture Guides & Utilities

• 📄 AST Chunking & Parent-Child Document Retrieval
• 🛠️ Interactive AI Agent Builder Lab & Code Studio

What retrieval costs per query

Agentic RAG re-ranks, re-queries and self-corrects, so one user question can mean several model calls. Cost per query, not cost per call, is the number to plan with. Below: one resolved query at 20K input tokens, 12K cached, 1.5K output. Of the 20,000 input tokens, 12,000 are billed at the cache-read rate and 8,000 at full input rate.

At twenty thousand queries a month the entire range fits inside a rounding error of most infrastructure bills, which is why the small models dominate this category. The self-correction loop is worth its cost precisely because it is cheap: an extra verification call costs a fraction of a cent on these models and a wrong answer does not.

Rates verified against provider documentation on September 18, 2026. Promotional rates expire, so re-check before budgeting: LLM API cost planning · September 2026 pricing update. Run your own numbers in the cost calculator.