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RAG, MCP and Agents Solve Three Different Problems

They get compared as if you pick one. You do not — they answer separate questions: what the model knows, what it can reach, and who decides what happens next.

Verdeshell Team · 7 min read · Last reviewed

RAG is about knowledge the model did not train on. MCP is about connecting a model to tools through one standard interface. Agents are about who chooses the next step. They compose.

RAG, MCP and agents answer three different questionsThree separate rows. RAG answers what the model knows: a question retrieves documents and the answer comes from them. MCP answers what the model can reach: one standard interface connects it to many tools. An agent answers who decides the next step: it takes a goal, chooses an action, observes the result and adjusts. A real system usually uses all three together.What does it KNOW?RAGQuestionRetrieve docsAnswer from themWhat can it REACH?MCPModelOne interfaceMany toolsWho DECIDES next?AgentGoalChoose actionObserve, adjustKnowledge, connectivity, control — different layers. Not a choice between three options.
Three separate questions. A real system usually answers all three at once.

RAG answers: what does the model know?

Retrieval-augmented generation puts relevant documents in front of the model at the moment of the request, so it can answer from your material rather than from training data alone.

The mechanics are consistent: split source documents into chunks, embed them into vectors, store them, and at query time retrieve the closest chunks and include them in the prompt alongside the question.

The reason it dominates enterprise deployments is that most business questions depend on private, current information the model has never seen — this quarter's policy, this customer's history, this codebase.

The honest caveat: RAG makes the right information available, it does not force the model to use it faithfully. Whether the answer is actually grounded in what was retrieved is a separate property that has to be measured separately.

MCP answers: what can the model reach?

The Model Context Protocol is an open standard for connecting AI models to external tools and data sources through one consistent interface, rather than a bespoke integration per model and per tool.

The problem it addresses is combinatorial. Without a standard, connecting several models to several systems means writing and maintaining an integration for each pair. A shared protocol turns that into one implementation per side.

MCP is plumbing, and worth being clear about that: it does not make a model smarter or more autonomous. It standardises how a capability is exposed. That is genuinely valuable and it is not the same kind of thing as RAG or agency.

Agents answer: who decides the next step?

An agent has a goal and picks its own actions toward it, observing results and adjusting. See the concepts piece on generative versus agentic for where the line sits.

The relevant point here is the relationship to the other two. An agent typically uses retrieval as one of its tools and may reach that tool over MCP. Retrieval and protocol are capabilities; agency is what decides when to use them.

How they compose in a real system

A support assistant might retrieve policy documents (RAG), reach the order system and the refund API through a standard interface (MCP), and decide for itself whether this particular case warrants a refund or an escalation (agent).

Written out, the "versus" framing stops making sense. The three sit at different layers: knowledge, connectivity, control.

Choosing what you actually need

If the failure is that the model does not know your information, you need retrieval. Adding autonomy will not help and will make wrong answers more consequential.

If the failure is that every integration is bespoke and the maintenance is crushing you, you need a protocol layer.

If the failure is that a person has to shepherd every step of a multi-step task, that is the case for agency — and the point at which you need the stop conditions and error handling covered in the building track.

Most teams reach for the third when their actual problem is the first.

A question through a retrieval pipelineA question is converted into an embedding and used to search a vector store built from the source documents. The closest passages are returned and placed in the model context alongside the original question. The model answers from those passages, and the answer is checked for grounding — whether it is actually supported by what was retrieved.Questionfrom a userEmbedto a vectorVector storeyour documentsTop passagesnearest matchesContextquestion + passagesModelanswersGrounded?supported by sources

1. Ask — A question arrives. On its own the model has never seen your policies, tickets or codebase.

Step 1 of 7
Read this walkthrough as text
  1. 1.Ask — A question arrives. On its own the model has never seen your policies, tickets or codebase.
  2. 2.Embed — The question becomes a vector, so it can be compared by meaning rather than by keyword.
  3. 3.Search — That vector is matched against the store built from your documents at indexing time.
  4. 4.Retrieve — The nearest passages come back. Chunk boundaries chosen at indexing time decide how useful they are.
  5. 5.Assemble — The passages go into the context alongside the original question. Nothing has been decided by the model yet.
  6. 6.Answer — The model answers from what it was given — and this is where retrieval stops helping.
  7. 7.Check grounding — Whether the answer is actually supported by the retrieved passages is a separate property, and has to be measured separately.
One question end to end — and the point where retrieval stops helping.

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