Agents are only as reliable as the context you give them. A model with no grounding will confidently invent APIs, prices and facts. Two tools change that: retrieval-augmented generation (RAG) and the Model Context Protocol (MCP).
RAG: grounding in your data
RAG retrieves relevant documents from a vector store and injects them into the prompt, so the model answers from real data instead of memory. The quality of the retrieval matters more than the size of the model.
MCP: a clean interface to tools
MCP standardizes how an agent talks to external tools and data sources. Instead of bespoke glue per integration, the agent speaks one protocol, which makes systems easier to audit and extend.
Putting them together
- Use MCP to expose tools and data sources behind one interface.
- Use RAG to ground answers in retrieved, verifiable context.
- Keep a verification step: never let an unchecked agent action reach production.
The goal isn't a smarter agent. It's a trustworthy one.