RAG vs MCP: The Architectural Difference Every AI Developer Must Understand

The provided code outlines a modular system for building an intelligent data engineering support agent that can both retrieve internal policy and runbook context (via RAG) and interact with live monitoring tools to fetch real-time pipeline status information. Below is a summary of the key components and their roles:

1. RAG Initialization (agent/openai_agent.py):

   • The system initializes by loading all .md files from a specified directory into memory, creating a combined string that serves as context for policy retrieval.
   • A RAGRetriever instance is created to index and retrieve relevant policies based on user queries.

2. LLM Router (agent/llm_router.py):

   • This component classifies the intent of incoming queries by checking against predefined keyword lists.
   • It determines whether a query requires RAG (retrieval of policy context), MCP (interaction with live monitoring tools), or both.

3. Live Monitoring Tools (tools.pipeline_status.py):

   • A mock function get_pipeline_run_status() simulates fetching real-time pipeline status data, which would be replaced by actual API calls in a production environment.
   • The function returns structured information about the current state of specified pipelines

Read the full article at Towards AI - Medium

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.