AI Agent Architecture Blueprint

Corrective Agentic RAG with Self-Reflection

Autonomous retrieval pipeline combining dense vector embeddings, knowledge graph traversal, and an iterative self-correction loop to diagnose and eliminate hallucinations.

1. System Requirements

Functional

Decompose complex multi-hop queries into targeted sub-questions with contextual dependency graphs.
Perform hybrid retrieval combining dense vector similarity (pgvector/Qdrant), keyword BM25, and entity graph traversal (Neo4j).
Grade document relevance autonomously with self-reflection: if chunks lack relevance, rewrite queries or trigger web search tools.

Non-Functional / SLA

Zero hallucination tolerance: every generated claim must map directly to an attributed source citation chunk.
P95 query response time under 1.8 seconds including reflection and self-correction loops.
Automatic fallback to external search tools (MCP Google Search / Tavily) when internal corpora lack coverage.

2. Scale & Resource Estimates

Throughput: 10,000 hybrid queries/minute across 50M document chunks.

Storage & RAM: 120 GB HNSW dense vector index + 45 GB Neo4j graph cluster + 20 GB Redis embedding cache.

3. System API Endpoints

POST /v1/agentic-rag/query
Content-Type: application/json

{
  "query": "How did Q3 cloud infrastructure spend change compared to our original 2025 budget?",
  "max_reflection_hops": 3,
  "confidence_threshold": 0.85
}

Response (200 OK):
{
  "answer": "Cloud infrastructure spend increased by 14.2% in Q3 2025 ($1.37M vs. budgeted $1.20M), primarily driven by GPU cluster provisioning for agent model inference.",
  "citations": [
    { "source": "2025_Q3_Financial_Review.pdf", "page": 14, "chunk_id": "chk-994", "score": 0.94 },
    { "source": "2025_Annual_Operating_Plan.xlsx", "sheet": "CapEx", "chunk_id": "chk-112", "score": 0.91 }
  ],
  "reasoning_trajectory": [
    "Step 1: Extracted budgeted Q3 cloud spend -> $1.20M",
    "Step 2: Extracted actual Q3 cloud spend -> $1.37M",
    "Step 3: Computed variance -> +14.2% ($170,000 increase)"
  ],
  "retrieval_hops": 2
}

4. Database Schema Design

CREATE TABLE rag_chunks (
    chunk_id VARCHAR(64) PRIMARY KEY,
    document_id VARCHAR(64) NOT NULL,
    text_content TEXT NOT NULL,
    embedding VECTOR(1536), -- pgvector HNSW index
    entities TEXT[],
    metadata JSONB,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

CREATE INDEX idx_rag_hnsw ON rag_chunks USING hnsw (embedding vector_cosine_ops);
CREATE INDEX idx_rag_doc ON rag_chunks(document_id);

5. Interactive High-Level Architecture

graph TD Query["User Multi-Hop Query"] --> Decomposer["Query Decomposer LLM"] Decomposer --> Search["Hybrid Retrieval: Vector, BM25, Graph"] Search --> Grader{"Relevance Grader LLM"} Grader -->|Relevant Score| Generator["Grounded Answer Generator"] Grader -->|Low Score| Rewriter["Query Rewriter / Web Tool Fallback"] Rewriter --> Search Generator --> HallucinationVerifier{"Hallucination Verifier"} HallucinationVerifier -->|Pass| VerifiedOutput["Verified Answer with Citations"] HallucinationVerifier -->|Fail| Generator

6. Scalability Deep Dive & Bottlenecks

Standard Naive RAG is brittle: it blindly injects the top-K retrieved text chunks into the prompt context, leading to hallucinations when chunks are tangential or contradictory. Agentic RAG introduces an active decision loop: an evaluator LLM grades each chunk's relevance. If the chunks fail the relevance threshold, the agent rewrites the search query (query expansion, entity extraction) and issues a secondary retrieval or falls back to live web search tools before drafting the final answer.

7. Production Scaling Strategies

Context Caching: Cache pre-computed embeddings and document index catalogs in memory using pgvector and Qdrant.

Reciprocal Rank Fusion (RRF): Merge rankings from sparse keyword search, dense semantic search, and knowledge graph neighbors with low CPU overhead.

Speculative Verification: Run hallucination checks asynchronously or in parallel with streaming to provide zero perceived latency overhead for end users.

8. Runnable Production Starter Code

Scaffold

System Design Architectures