The one AI skill that matters: agentic RAG pipelines with sub-agent orchestration.

A production grade agentic RAG pipeline replaces human driven knowledge work (such as underwriting, contract review, competitive intelligence, or compliance audits) by combining multi agent orchestration, specialized retrieval, and deterministic validation loops.

Below is a system architecture designed for enterprise scale autonomous execution.

Core System Architecture

                                  [ User / Trigger Event ]
                                             │
                                             ▼
                             ┌───────────────────────────────┐
                             │    1. Orchestrator / Router   │
                             │  Task Breakdown               │
                             │  Dynamic DAG State Graph      │
                             └───────────────┬───────────────┘
                                             │
              ┌──────────────────────────────┴──────────────────────────────┐
              ▼                                                             ▼
 ┌─────────────────────────┐                                   ┌─────────────────────────┐
 │   2A. Extraction Agent  │                                   │    2B. Analysis Agent   │
 │   Hybrid RAG Retr.      │                                   │   Multi Hop Retrieval   │
 │   Structural Parsing    │                                   │   Domain Reasoning      │
 └────────────┬────────────┘                                   └────────────┬────────────┘
              │                                                             │
              └──────────────────────────────┬──────────────────────────────┘
                                             ▼
                             ┌───────────────────────────────┐
                             │  3. Validation & Guardrails   │
                             │  Faithfulness / Grounding     │
                             │  JSON Schema Enforcement      │
                             │  Deterministic Rule Engine    │
                             └───────┬───────────────┬───────┘
                                     │ Fail          │ Pass
                                     ▼               ▼
                        ┌──────────────────┐   ┌───────────────────────────┐
                        │ Reflection Loop  │   │   4. Consensus & Action   │
                        │ (Self Correction)│   │   Side Effect Tool Calls  │
                        └────────┬─────────┘   │   Human in the Loop (HITL)│
                                 │             └───────────────────────────┘
                                 └─► Retry Limit Exceeded? ──► Escalate to Human

Key Architectural Layers

1. Orchestrator & Task Decomposition (Meta Agent)

• Graph based State Machine: Implement execution graphs using stateful engines like LangGraph, Temporal, or LlamaIndex Workflows. Avoid linear chains; workflows must be cyclic to allow retries.
• Task Graph Generation: The orchestrator converts incoming unstructured requests into a typed Directed Acyclic Graph (DAG), mapping tasks to specialized sub agents with strict inputs and outputs.
• Shared Working Memory: A transactional Redis or Postgres state store maintains the global execution trace, allowing sub agents to read upstream outputs and write isolated local states without context pollution.

2. Sub Agent RAG Layer (Domain Workers) Each sub agent functions as an independent specialist targeting a dedicated domain silo rather than a single massive vector database:

• Hybrid Retrieval (Sparse + Dense): Integrates BM25 with dense vector embeddings (e.g., pgvector, Qdrant, or Pinecone), fused via Reciprocal Rank Fusion (RRF).

• Chunking & Indexing Strategy:

• Document Trees / Hierarchical Indexing: Small chunk retrieval for accurate semantic matching linked back to parent document nodes for full context injection.

• Metadata Partitioning: Hard filtering on company entity, fiscal period, or compliance jurisdiction prior to vector similarity calculation.

• Query Transformation & Multi Hop: Sub agents leverage sub query expansion and HyDE (Hypothetical Document Embeddings) to retrieve contextual evidence across multiple passes.

3. The Validation Engine (Output & Hallucination Defense) To automate tasks previously staffed by teams of analysts, the system must enforce rigorous quality gates before triggering side effects:

4. Reflection & Corrective Routing

• Self Correction Loop: When an output fails verification, the validator attaches diagnostic feedback (e.g., "Field 'ebitda_margin' does not match source table on page 14"). The sub agent re runs its retrieval and synthesis using this explicit correction context.
• Failure Circuit Breaker: Cap reflection iterations (typically $N=2$ or $3$). If the sub agent fails repeatedly, route the state snapshot to an asynchronous Human in the Loop (HITL) review queue.

5. Execution & Action Integration

• Idempotent Side Effects: Real world work requires writing to ERPs, CRMs, or drafting contracts. All tool calling actions must be strictly idempotent to prevent duplicate operations during agent replays.
• Auditability & Provenance: Every atomic output field carries a citation envelope containing the document hash, page/byte coordinates, and retrieval timestamp for compliance auditing.

Production Implementation Blueprint

from pydantic import BaseModel, Field
from typing import List, Literal, Optional

# 1. Deterministic Output Schema
class FinancialMetric(BaseModel):
    metric_name: str
    value: float
    unit: str
    page_reference: int
    source_quote: str

class ExtractionResult(BaseModel):
    company_name: str
    fiscal_year: int
    metrics: List[FinancialMetric]
    confidence_score: float = Field(ge=0.0, le=1.0)

# 2. Validation Rule Engine
class GroundingValidator:
    @staticmethod
    def verify_citations(extraction: ExtractionResult, raw_chunks: dict) -> tuple[bool, str]:
        for item in extraction.metrics:
            chunk = raw_chunks.get(item.page_reference, "")
            # Verify direct evidence or semantic entailment
            if item.source_quote not in chunk:
                return False, f"Quote for {item.metric_name} not found in page {item.page_reference}"
        return True, "Passed"

# 3. Agent Execution Node Flow (e.g., in LangGraph)
def extraction_node(state: dict) -> dict:
    retrieved_docs = hybrid_retriever(state["current_task"])
    raw_output = llm_generate(
        system="Extract key figures adhering to schema.",
        prompt=f"Task: {state['current_task']}\nContext: {retrieved_docs}",
        response_model=ExtractionResult
    )
    
    valid, message = GroundingValidator.verify_citations(raw_output, retrieved_docs)
    
    if not valid:
        state["retry_count"] += 1
        state["feedback"] = message
        state["next_node"] = "reflector_node" if state["retry_count"] < 3 else "hitl_node"
    else:
        state["data"] = raw_output
        state["next_node"] = "action_node"
        
    return state

Critical Operational Practices

• Context Isolation: Never dump all documents into the top level orchestrator. Keep the orchestrator lightweight (handling only state routing and summaries) while delegating document reading strictly to sub agents.
• Latency vs. Accuracy Tiering: Use smaller, faster models (e.g., 8B–70B parameter models) for preliminary chunk filtering and semantic routing, while reserving frontier grade models for multi hop synthesis, extraction, and validation.
• Continuous Eval Driven Ingestion: Benchmark validation rules against a golden evaluation dataset with synthetic perturbations to verify that hallucinations are caught before the pipeline interacts with external APIs.