What’s the Difference — and Why It Matters for Your Business
Everyone’s talking about AI right now — but not everyone’s using the same language.
You’ve heard:
- “We built an AI agent.”
- “It’s powered by an LLM.”
- “It uses context — we integrated MCP.”
And if you’re like most business owners, you’re wondering:
What does any of this actually mean for me?
This blog breaks down the difference between LLMs, Agents, and MCP, using clear language and real examples — so you know what matters when you’re thinking about AI for your business.
✅ First, What Is an LLM?
LLM = Large Language Model
It’s the brain behind many modern AI tools — like ChatGPT or Claude.
LLMs:
- Take input (your question or request)
- Predict the most relevant output (a response)
- Learn from patterns in language, data, and behavior
- Can generate text, analyze content, summarize, explain, brainstorm, and more
But here’s the catch:
LLMs don’t remember what they did five minutes ago.
They don’t know who you are, what your business does, or what’s already happened — unless you tell them every time.
✅ Then, What’s an AI Agent?
An AI agent is a step up from an LLM. It uses an LLM plus logic to take actions.
Agents:
- Break down goals into steps
- Use tools (like calendars, databases, or APIs)
- Make decisions based on outcomes
- Can operate semi-autonomously
Imagine an AI agent like an employee with basic training.
It uses the LLM’s brain to think, but it also:
- Chooses tools
- Checks if something worked
- Moves to the next task
But agents still have a problem:
They’re forgetful, and they don’t always share information well.
That’s where MCP comes in.
✅ So, What Is MCP?
MCP = Model Context Protocol
It’s the framework that helps LLMs and agents work together, with memory and structure.
It gives them:
- User context (who you are, what role you play)
- Task context (what’s happening, what’s next)
- System context (what tools are being used, what’s already done)
- Business logic (rules, preferences, constraints)
MCP is like the office handbook — it keeps everyone on the same page, even when using different tools or agents.
Without it:
- AI forgets things
- Tools work in silos
- Agents make redundant decisions
- You start repeating yourself across platforms
With it:
- AI systems act in sync
- Agents pass data between steps and systems
- LLMs get the context they need — without you re-explaining
🧠 Simple Analogy: Your Team at Work
Let’s say you’re running a project. Here’s how the roles play out:
| Role | What it Does |
|---|---|
| LLM | The team’s smartest thinker — great with language, can write anything, but doesn’t remember yesterday unless reminded |
| Agent | The team member who takes instructions and completes tasks — but only based on what they know in the moment |
| MCP | The internal wiki, SOPs, calendars, and shared chats — where the full picture lives, and everyone can access it |
The more they share context, the smoother things go.
That’s what MCP enables — across tools, platforms, and AI systems.
Why It Matters for Your Business
Most companies exploring AI right now are using:
- Chatbots with no memory
- Automations that don’t talk to each other
- Smart tools that can’t share logic
At Prototype Toronto, we help teams upgrade to systems that:
- Think with LLMs
- Act with agents
- Stay smart with structured context using MCP
This is how you go from “fun demo” to AI that actually saves time, reduces errors, and fits into your business operations.
Want Help Building an AI System That Actually Works Together?
We help founders and teams build real AI workflows — using LLMs, agents, and MCP when it makes sense.
📩 Contact us to talk to our AI Integration & Automation team — and let’s make your AI systems make sense together.
