MCP vs RAG vs AI Agents

Source: https://www.linkedin.com/posts/alexxubyte_systemdesign-coding-interviewtips-activity-7427754721752588288-stjy?utm_source=share&utm_medium=member_desktop&rcm=ACoAABLmv3sBy_3Uhuij4CbqcQrhEbDtwAIqTOY

MCP, RAG, and AI Agents: Understanding the Differences | Alex Xu님이 토픽에 대해 올림 | LinkedIn

 

 

MCP vs RAG vs AI Agents

Everyone is talking about MCP, RAG, and AI Agents. Most people are still mixing them up. They’re not competing ideas. They solve very different problems at different layers of the stack.

MCP (Model Context Protocol) is about how LLMs use tools. Think of it as a standard interface between an LLM and external systems. Databases, file systems, GitHub, Slack, internal APIs.

Instead of every app inventing its own glue code, MCP defines a consistent way for models to discover tools, invoke them, and get structured results back. MCP doesn’t decide what to do. It standardizes how tools are exposed.

RAG (Retrieval-Augmented Generation) is about what the model knows at runtime. The model stays frozen. No retraining. When a user asks a question, a retriever fetches relevant documents (PDFs, code, vector DBs), and those are injected into the prompt.

RAG is great for:
- Internal knowledge bases
- Fresh or private data
- Reducing hallucinations

But RAG doesn’t take actions. It only improves answers.

AI Agents are about doing things. An agent observes, reasons, decides, acts, and repeats. It can call tools, write code, browse the internet, store memory, delegate tasks, and operate with different levels of autonomy.

--
Subscribe to our weekly newsletter to get a Free System Design PDF (368 pages): https://lnkd.in/gauQcE45