There are roughly four ways a company tries to get AI into production. Most founders and executives default to one of the first three without realizing it — and each one has a predictable failure mode that shows up right around the time it matters most.
This post runs a SWOT analysis on all four. The goal is not to declare one model universally correct. It is to make the tradeoffs visible before you commit.
An LLM is only as useful as the context it has. The model itself is frozen — its weights were fixed at training time. Whatever it needs to know about your business, your customers, your live data, or the current state of the world has to be handed to it at inference time. Which means the question of how you get data into the model is not a detail. It is a core architectural decision.
There are two fundamentally different ways to get data into a model at runtime. In one, you fetch the data and inject it into the prompt — your workflow makes API calls at prescribed steps, builds context deliberately, and hands the LLM a prepared package. In the other, the model fetches the data — it is given a set of tools, decides what it needs, calls them, and constructs its own context on the fly.
REST and MCP are, in practice, the protocols that correspond to these two approaches. REST is the workhorse of the first. MCP is the infrastructure of the second. And that distinction maps almost exactly onto the autonomous vs. deterministic question from the previous post. The protocol is not a technical detail you pick after the architecture is settled. It is an architecture decision.
The term "agent" is everywhere in AI right now. It's been used to describe everything from autonomous coding assistants that can spin up entire codebases, to simple scripts that restart a server when it goes offline. This ambiguity creates confusion, especially for enterprises trying to figure out what "agentic AI" actually means for their business.
Let's clear that up. In practice, most "agents" fall into one of three categories.