Master AI System Architecture: RAG, MCP, A2A, Agents, etc – CG群

精通AI系统架构：RAG、MCP与Agent设计

本课程系统讲解AI系统架构设计，涵盖RAG、MCP、A2A及Agent等核心概念。从中级水平出发，深入网络通信、容器部署与性能优化，帮助你构建可扩展、安全的AI应用流水线。适合希望掌握现代AI基础设施的工程师。

Master AI System Architecture: RAG, MCP, A2A, Agents, etc

The Complete Guide to AI System Design, Inference and Agent Architecture

What you’ll learn
⚡ Design scalable AI system architectures that integrate machine learning models, cloud infrastructure, APIs, and distributed computing environments.
⚡ Understand and implement networking concepts essential for AI systems, including client-server communication, load balancing, latency optimization, and secure d
⚡ Deploy and manage AI applications using modern infrastructure tools such as containers, microservices, virtualization, and cloud platforms.
⚡ Build reliable and secure AI pipelines by applying best practices in networking security, system monitoring, fault tolerance, and performance optimization.
⚡ Analyze real-world AI infrastructure challenges and develop end-to-end solutions for enterprise-grade AI systems, from data ingestion to model deployment and ne

Requirements
❗ An interest in artificial intelligence, technology, or modern digital systems will help learners get the most from the course.
❗ A willingness to explore new concepts and practical AI technologies is the only essential requirement for success in this course.
❗ Basic understanding of programming concepts, is recommended.
❗ Foundational knowledge of operating systems and cloud computing basics, is recommended
❗ Familiarity with fundamental computer networking concepts such as IP addressing, protocols, and client-server architecture will be helpful.

Description
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 Network

✨ Transformer Architecture

✨ Large Language Models (LLMs) Vs Small Language Model (SLMs)

✨ Retrieval-Augmented Generation (RAG)

✨ Vector Search & Embeddings

✨ Tool Calling Systems

✨ KV Cache

✨ AI Memory Architectures

✨ Network and Security Considerations

✨ MCP (Model Context Protocol)

✨ A2A (Agent-to-Agent Communication)

✨ MCP + A2A Integration

✨ AI Security Patterns

✨ AI Inference Infrastructure

✨ Multi-Agent Systems

✨ Architecture 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

✨ Create production-ready AI application architectures

✨ Evaluate AI infrastructure tradeoffs and scalability patterns

This course focuses on practical architecture thinking rather than just theory — helping you understand how real AI products are engineered in modern companies.

Whether you are building AI copilots, enterprise assistants, autonomous agents, intelligent search systems, or next-generation AI platforms, this course will give you the architectural foundation needed to design modern AI systems with confidence.

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