Agentic RAG System: Multi-Agent Retrieval and Reasoning Workflow with n8n

Project Pitch:
I built an agentic RAG system that doesn't just retrieve information — it thinks, reasons, and double-checks like a smart research assistant.
It breaks user questions into parts, routes each part to the right domain expert agent, uses business glossaries and metadata, and combines the outputs into high-quality, verified answers.
It even asks itself follow-up questions if needed to ensure the user always receives a precise, domain-aware response.

---

📊 System Diagram

This workflow uses intelligent agent routing and fallback mechanisms:

---

What It Does

• Breaks complex questions into smaller sub-questions

• Routes each sub-question to the best domain expert agent using metadata and glossaries

• Pulls from multiple retrieval sources (knowledge bases, vector stores)

• Synthesizes final answers, checking consistency and completeness

• Uses a Supervisor Agent to manage the conversation intelligently and autonomously

• 🖼️ Visual Comparison: Agentic RAG vs. Single LLM

This side-by-side shows how agent-based architecture distributes tasks intelligently, while a single LLM must handle everything in one shot.

---

Technologies Used

• n8n (workflow orchestration)
• OpenAI GPT models via LangChain
• Vector search with Pinecone or Weaviate
• Metadata filtering and glossary matching
• Agentic orchestration strategies (Supervisor Agent, Specialist Agents, Follow-up Question Generation)

---

Files

• agentic-rag-system-workflow.json — The exported n8n workflow file
• agentic-rag-system-diagram.png — Visual flow diagram of the system

---

Why This Matters

This project shows how modern AI systems can go beyond simple question answering — building agentic, multi-step, self-verifying reasoning flows that ensure higher quality, domain-specific answers.
It demonstrates practical AI orchestration skills with real business impact potential.

---

Future Work

• Implement a dynamic Supervisor Agent that adapts its aggregation logic based on domain-specific metadata
• Integrate document freshness scoring to prioritize recent information retrieval
• Add an automated retraining system to update the domain experts based on new data
• Deploy a monitoring dashboard to track agent performance, answer latency, and user satisfaction
• Build an agent evaluation dashboard for performance benchmarking

---

Demo built for AI Agent Implementation Manager portfolio presentation.