RAG vs MCP vs A2A: Understanding the Modern AI Stack

The AI Stack in 2026

The modern AI landscape has evolved beyond simple chatbots into a sophisticated multi-layer architecture. Understanding where RAG, MCP, A2A, and Agentic AI fit is crucial for any engineering team building intelligent systems.

RAG: The Memory Layer

Retrieval-Augmented Generation gives AI models access to external knowledge. Instead of relying solely on training data, RAG systems retrieve relevant documents from vector databases before generating responses.

MCP: The Tool Layer

Model Context Protocol, created by Anthropic, standardizes how AI models connect to external tools and services. Think of it as USB-C for AI.

A2A: The Communication Layer

Google's Agent2Agent protocol enables AI agents to discover and collaborate with each other regardless of their underlying framework.

Agentic AI: The Brain

The orchestration layer that plans, reasons, and executes multi-step workflows using all the layers below it.

Putting It All Together

These technologies aren't competitors—they're complementary. A production AI system might use an agentic framework to orchestrate workflows, RAG to retrieve knowledge, MCP to execute tools, and A2A to coordinate with specialized agents.