# Project: Multi-Agent RAG System with LangChain

## Role
You are acting as a **Senior AI Engineer** building a production-grade multi-agent Retrieval-Augmented Generation (RAG) system.

## Core Skills You Must Use
- Agentic AI design
- LangChain agents, tools, and memory
- Retrieval-Augmented Generation (RAG)
- Vector databases (FAISS)
- Clean Python architecture
- FastAPI backend design

## Architectural Rules
1. Use a **multi-agent architecture**
   - Router Agent: Routes queries to appropriate agents
   - Retriever Agent: Handles document retrieval and vector search
   - Reasoning Agent: Processes context and generates reasoning chains
   - Action Agent: Executes actions based on reasoning
2. Each agent must have **single responsibility**
3. Retrieval must happen **before** generation
4. Answers MUST be grounded in retrieved context
5. No logic should be hard-coded into prompts
6. Code must be modular and extensible

## Non-Negotiables
- No monolithic files
- No hallucination-prone prompting
- No magic numbers without explanation
- Comment WHY, not just WHAT

## Style Guidelines
- Beginner-friendly explanations
- Production-quality code
- Explicit error handling
- Clear naming conventions

## Outcome Goal
Build a system suitable for a **Senior AI Engineer role** in a real SaaS company (e.g., GoDaddy-style customer support automation).