Agentic RAG & Advanced AI Workflows: Multi-Step Research, Planning, Retrieval & Verification

Complex user requests cannot be passed verbatim to a vector database. The first step is Task Decomposition: breaking down an ambiguous objective into an explicit Directed Acyclic Graph (DAG) of subtasks:

Without decomposition, the LLM hallucinates generalized business platitudes (“Customers switched due to competitive market pressures”) without empirical data.

The Fundamental System Rule: RAG is for semantic knowledge; SQL is for structured facts; Code is for arithmetic. Never query a vector database for numerical aggregation:

In traditional RAG, if a retriever fetches 5 irrelevant documents, the model generates an answer based on noise. Corrective RAG (CRAG) injects an automated relevance grader between retrieval and generation:

If the user asks “Why did customer retention decline?” and the top retrieved chunk is the company's 2018 founding history, the CRAG grader rejects the chunk, transforms the query into ["customer churn metrics", "churn post-onboarding"], and re-executes retrieval.

Enterprise research questions frequently require following a breadcrumb trail across multiple entities:

• Hop 1: Query Q3 Executive Report → Discovers “Project Titan was delayed by 6 weeks.”
• Hop 2: Query Architecture Docs for Project Titan → Discovers “Titan depends on the Dublin Data Center migration.”
• Hop 3: Query Infrastructure Incident Log → Discovers “Fiber cut in Dublin on August 14th caused 4-day outage.”

Contradiction Resolution in Evidence Stores

When Source A says “Product Price = $49/mo” and Source B says “Product Price = $69/mo”, a naive LLM will either guess or fabricate an average ($59). An agentic system surfaces the conflict in an Evidence Store:

In production systems, separating the Planner from the Executor allows asynchronous parallelization and strict policy guardrails. If an agent needs sales figures, inventory status, and support complaints, executing them sequentially wastes seconds of latency. Parallel fan-out cuts wall-clock latency by up to 60%:

One of the most expensive errors in modern AI engineering is delegating deterministic computation to LLMs. Never ask an LLM to calculate percentage growth, sort 500 records, or verify tenant IDs:

The following provider-neutral implementation demonstrates an Agentic RAG orchestrator with dynamic routing, corrective document grading, and evidence verification:

import asyncio
from typing import List, Dict, Any

class AgenticRAGOrchestrator:
    def __init__(self, router, vector_db, sql_db, grader, verifier, llm):
        self.router = router
        self.vector_db = vector_db
        self.sql_db = sql_db
        self.grader = grader
        self.verifier = verifier
        self.llm = llm

    async def execute_task(self, user_goal: str, tenant_id: str) -> Dict[str, Any]:
        # Step 1: Decompose Goal into Structured Subtasks
        plan = await self.llm.generate_plan(user_goal)
        evidence_store = []

        for subtask in plan.subtasks:
            # Step 2: Route Subtask to Optimal Data Engine
            route = self.router.classify(subtask.query)
            
            if route == "SQL":
                # Execute deterministic SQL query
                sql_result = await self.sql_db.execute_safe(subtask.sql_query, tenant_id=tenant_id)
                evidence_store.append({"type": "data", "content": sql_result, "source": "PostgreSQL"})
                
            elif route == "RAG":
                # Execute vector retrieval with Corrective RAG (CRAG)
                docs = await self.vector_db.search(subtask.query, filter={"tenant_id": tenant_id})
                relevance_score = self.grader.evaluate(docs, subtask.query)
                
                if relevance_score < 0.70:
                    # Trigger query rewriting loop
                    rewritten_query = await self.llm.rewrite_query(subtask.query)
                    docs = await self.vector_db.search(rewritten_query, filter={"tenant_id": tenant_id})
                    
                evidence_store.append({"type": "knowledge", "content": docs, "source": "Knowledge_Base"})

        # Step 3: Verify Claims Against Evidence Store
        draft = await self.llm.synthesize_response(user_goal, evidence_store)
        verification = self.verifier.check_support(draft, evidence_store)
        
        if not verification.is_fully_supported:
            draft = await self.llm.revise_unsupported_claims(draft, verification.missing_claims)

        return {"answer": draft, "evidence": evidence_store, "verified": True}