Agentic RAG Architecture
Multi-hop query decomposition, dynamic query reformulation, and autonomous citation critique for high-complexity enterprise research.
Table of Contents (9 sections)
The Core Problem Solved
Traditional one-shot RAG fails when a user question requires aggregating information across disparate systems, comparing temporal periods, or reconciling conflicting data.
When To Deploy This Architecture
Complex enterprise analytical research, multi-document financial audit reconciliation, and cross-system policy verification.
Architectural Components
- Plan-and-Solve Query Decomposer
- Tool Registry (Vector DB, SQL Database, REST APIs, Web)
- Reflection & Self-Correction Evaluator
- State Management & Memory Store
- Deterministic Guardrails & Timeout Fence
Data Flow Narrative
Security & Perimeter Control
Strict sandboxing of all tool execution environments; read-only credentials on data connectors; explicit approval steps before any state-mutating action.
Governance & Telemetry
Graph-based trace recording (LangSmith / OpenTelemetry) capturing each agent reasoning step and decision branch.
Identified Failure Modes & Mitigations
Recursive loop death spirals; tool schema drift; semantic divergence where agent answers a different question than asked.
Production Code Implementation
Copy-paste ready implementation snippet demonstrating the core architectural boundary logic:
# Bounded Agentic RAG State Graph with Deterministic Fences
from typing import TypedDict
from langgraph.graph import StateGraph, END
class AgentState(TypedDict):
query: str
sub_queries: list[str]
retrieved_chunks: list[dict]
iteration: int
final_answer: str
def plan_node(state: AgentState):
return {"sub_queries": decompose_query(state["query"]), "iteration": state["iteration"] + 1}
def retrieve_node(state: AgentState):
chunks = []
for sq in state["sub_queries"]:
chunks.extend(hybrid_retrieve(sq, top_k=3))
return {"retrieved_chunks": deduplicate_and_trim_rbac(chunks)}
def evaluate_gate(state: AgentState):
if state["iteration"] >= 3:
return "synthesize" # Deterministic loop fence
return "synthesize" if evaluate_groundedness(state) > 0.85 else "refine"
workflow = StateGraph(AgentState)
workflow.add_node("plan", plan_node)
workflow.add_node("retrieve", retrieve_node)
workflow.add_conditional_edges("retrieve", evaluate_gate, {"refine": "plan", "synthesize": "synthesize"})Frequently Asked Questions
When should an enterprise use Agentic RAG instead of one-shot RAG?
How do you avoid infinite reasoning loops in multi-hop agent workflows?
The Enterprise AI Production Checklist
A rigorous 50-point engineering, security, and FinOps verification gate before promoting Generative AI and Agentic systems to live enterprise traffic.
Bhavin Mistry
Senior Engineering Manager at Commonwealth Bank based in Melbourne, Australia. Focusing on enterprise AI architecture, hybrid RAG, agentic reliability, and technology economics.
Cross-Referenced Production Resources
5-Stage Maturity Framework
Stage gates from exploration to hardened multi-region deployment.
AI Readiness Diagnostic
Benchmark organizational readiness across 12 operational dimensions.