Four Ways to Give AI Agents Knowledge: Skills, MCP, RAG, and Memory

We explore four approaches to equipping AI agents with task-specific knowledge beyond their training data: skills, MCP, RAG, and memory. Using a real-world example of resolving a 500 internal server error on a checkout page, we compare how each method works and when it is most effective.

■ Four methods for AI agent knowledge
- Skills: repeatable procedures with built-in judgment, triggered only when needed via progressive disclosure
- MCP: a standard protocol that connects agents to external systems such as logging stacks and dashboards
- RAG: on-demand retrieval of relevant documents from a vector database through semantic search
- Memory: knowledge the agent accumulates from past experiences, including writing back fixes for future use

■ Choosing the right approach
- Written knowledge → RAG; experience → memory; repeatable procedure → skill; external lookup → MCP

This overview suits developers and AI practitioners looking to design more effective agent workflows. By the end, you will be able to distinguish these knowledge mechanisms, decide which one fits a given task, and combine them for better problem-solving.