MCP, RAG, Agents? A 5-Minute Guide for Busy Innovators
A Business-Friendly Guide to Agents, Tool Calling, RAG, MCP, and good Vibes
Picture this:
You just came back from a very important business meeting.
You saw a demo where someone said:
“Our multi-agent-based architecture empowers dynamic execution across multi-modal contexts using real-time vectorized retrieval across our entire domain of organizational units”.
Hence it’s all automated now and we need only half the folks. AI Boom!
And you went:
“YES. Please. I want this. Get me this, now.” 🤩
You get back to the office, round up your engineering team together with the CTO and shout:
People…now is the time! I want Agents! I want that Vibe stuff! Let’s go!
You’re excited. You feel like a kid in front of the new Ben & Jerry’s flavor called Agentic Cookie Crunch.
Then your engineers start nodding slowly, some look confused and some start sweating.
You don’t mind — you just know that you want to ride the AI way like it’s 1999 and someone said “the internet”.
STOP.
That is when I walk in.
Your translator and tech whisperer, the guide through the jungle of buzzwords. And I say:
“Let me break this down so you understand what the heck is going on”.
This is your crash course in understanding what your engineers are talking about and how to turn all that tech magic into real business impact.
🤖 What is an Agent, Really?
Let’s start with the word that we used to fear whenever watching Matrix:
Agent
An agent is NOT just your chatbot that is just a bit “cooler”.
An agent is an AI that does not just generate text and talks to you — it “thinks”, “decides” and “acts”.
So what is the difference?
Well If you give a Chatbot the following text:
“Schedule meetings with our top 10 leads from last week and send them a thank-you email with their report” — it might answer with:
”That’s a cool idea! You want me to make a list for you?”
But an agent? It would actually do it.
- Check your CRM
- Prioritize the leads
- Sync Calendars and schedule meetings
- Send Emails
Agents are AIs with capabilities — like digital employees who don’t sleep and occasionally get your company slogan wrong (hallucinate).
🛠 Tool Calling or Make it do other stuff besides generating overly kind chat
So how exactly does the agent do stuff?
Tool calling means the AI can actually use things like calling APIs, searching Databases, accessing your CRM, calendar and even your coffee machine if you’d want to.
Basically providing the AI with the hands to do stuff now.
Before ChatGPT was just a brain in a jar.
Now we gave it hands to press buttons, run searchers, trigger workflows etc.
My prototype could simply send an email and update an asana task whenever a new customer was chatting with our agent.
How? I built this: https://medium.com/codex/ai-powered-customer-support-the-ultimate-multi-agent-system-524ba369fb2a
What that means?
When your engineer says:
“We’ll expose the email system as a tool for the agent to call”
what they mean is:
“The AI can now actually send emails.”
But yes, limiting what tools it’s allowed to use is a very good idea.
You’re giving it the keys but please not to your favorite car. Yet!
Why this matters to you: It turns your AI from a talker into a doer.
📚 RAG or How to make it “know” the stuff I need it to know
Basically this is about making AI google stuff for itself, sorta.
Let’s say the agent needs to answer a support question like:
“Does our enterprise plan include slack integration?”
Well the AI has been trained on lots and lots of data, but it does not know that. This is your specific information. And you don’t want to retrain it every time you update a document, right? This is way too expensive.
That is where RAG comes into play.
Retrievel-Augmented-Generation, which sounds extremely fancy and means:
Before answering this question, look up the answer in our actual documents, then response based on what you found.
So RAG = Search for Knowledge + ChatGPT.
You engineers will need a way to make sure that your data is structured and saved somewhere safe for your AI to work with…mention Vector Databases. Leave the rest to them now.
🧠 MCP or the USB-C for the agentic world
MCP (Model Context Protocol) is a protocol, duh, to make AI work with each other.
Imagine you have five different agents from different teams or companies:
- One that reads documents
- One that calls APIs
- One that summarizes meetings
- One that sends Slack messages
- And one that glues it all together
Each one needs to know:
- What tools are available
- How to call those tools
If they all used their own format for that, you’d get total chaos — like five people speaking five languages in a call.
That’s where MCP steps in.
MCP is a communication protocol that defines how AI agents and tools share tool calling context in a structured way.
It ensures:
- Different tools and models can be swapped in or out
- Your system stays modular, reusable, and extensible
Why should you care as a business person?
Because this is what makes scalable agent-based platforms possible.
Without MCP, your AI stack becomes a spaghetti mess of fragile prompts and hardcoded assumptions.
With it? You get a composable, future-proof AI architecture — one that can grow with your business.
🎸 Vibe Coding — Jam Sessions with AI
Now this one’s fun!
You might hear your engineers say:
“Let’s just vibe it out.”
What they mean is:
“Let’s prototype something fast with AI’s help and see what sticks.”
Vibe Coding is what happens when devs start building by talking to AI:
“Hey Copilot, scaffold me a web form that does X.”
“Now add validation for email and phone.”
“Now make it pretty.”
“Now add dark mode. Because, obviously that's cooler.”
It’s messy, rapid, creative — and perfect for getting ideas off the ground fast.
Business-wise it means:
- Shorter time to MVP
- More collaboration between PMs and engineers
- Fewer “wait 4 weeks to see anything” moments
Buuut here comes the BIG BUT.
It’s not for production-ready code!!!Thank me later.
You can not use most of that for your business code.
But it is how ideas come to life in hours instead of months.
🧩How It All Fits Together
Let’s say you want to build an AI that:
- Reads a support ticket
- Understands the problem
- Looks up the right internal doc
- Suggests a solution
- Sends the customer a personalized reply
- And schedules a follow-up if needed
Here’s the AI stack your engineers will build:
Agent: The brain that takes the goal and breaks it into actions
Tool Calling: Lets the agent actually do stuff, like send emails or update Jira
RAG: Helps the agent find up-to-date answers from docs
MCP: Gives the agent access to various tools and how to use them
Vibe Coding: How the team prototypes and iterates fast on the ideas
You don’t need to remember all the acronyms.
You just need to know this is how AI works behind the scenes.
So… What Now?
You had the vision.
Your engineers used the buzzwords.
Now you’ve got the translation.
Next time you hear someone pitch “agentic workflows” you can smile, nod… and ask:
So, you are using RAG? How are your tools exposed?
Welcome to the inner circle.
TL;DR:
Agent = AI with goals + tools + decision-making
Tool Calling = Gives AI real-world powers (send email, update CRM)
RAG = “Let me look that up first” — AI that checks your docs
MCP = Lets AI call different tools and knows how to do so
Vibe Coding = AI-assisted prototyping jam session
