Master AI Systems Architecture: RAG, MCP, A2A, Agents, etc.

About this course

AI System Architecture — Build Scalable AI Applications & Agentic Systems

Master the architecture behind modern AI applications — from LLMs and Transformers to RAG, MCP, A2A, vector databases, memory systems, inference infrastructure, and autonomous AI agents.

This course is designed for software engineers, AI engineers, architects, technical leaders, and developers who want to understand how production-grade AI systems are actually designed and deployed at scale.

You’ll learn the core building blocks that power today’s intelligent applications, including:

Neural NetworkTransformer Architecture

Large Language Models (LLMs) Vs Small Language Model (SLMs)Retrieval-Augmented Generation (RAG)Vector Search & Embeddings

Tool Calling SystemsKV CacheAI Memory Architectures

Network and Security ConsiderationsMCP (Model Context Protocol)A2A (Agent-to-Agent Communication)MCP + A2A IntegrationAI Security PatternsAI Inference Infrastructure

Multi-Agent SystemsArchitecture Anti Patterns

Enterprise AI System Design

Throughout the course, you’ll explore high-level architecture diagrams, real-world AI workflows, scalable deployment patterns, and modern design principles used in enterprise AI platforms.

By the end of this course, you will be able to:

• Design end-to-end AI system architectures
• Understand how modern LLM applications work internally
• Build scalable agentic AI workflows
• Architect RAG pipelines using vector databases
• Design AI memory and context systems
• Implement MCP and A2A communication models
• Understand inference optimization and token streaming
• Cre

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