RAG, MCP, Skills, and Memory: The Four Mechanisms That Give AI Agents Real-World Knowledge

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Large language models are limited to their training data by default, leaving them unable to access live systems, recall past interactions, or follow team-specific procedures. Engineers have converged on four distinct mechanisms to close this gap: Retrieval-Augmented Generation (RAG), the Model Context Protocol (MCP), Agent Skills, and agent memory. RAG grounds model responses in static documents like manuals or policy handbooks by retrieving relevant passages before generation, a technique rooted in a 2020 paper from Facebook AI Research, UCL, and NYU. MCP, open-sourced by Anthropic in November 2024, is a standardized client-server protocol that lets agents connect to live systems such as databases and internal APIs to read or write real-time data. Agent Skills and memory address the remaining gaps — repeatable procedures and the ability to retain context across sessions — making all four mechanisms complementary rather than interchangeable.

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