Confused by AI Terms? A Simple City Guide to Understanding

If you've spent any time exploring modern AI, you've probably noticed an explosion of new terms. Model Context Protocol (MCP). Agents. Studios. Builders. Knowledge Graphs. Connectors. APIs. CLIs.

Whether you're using ChatGPT, Claude, Gemini, Microsoft Copilot, or another enterprise AI platform, the technology itself is becoming easier to use. The naming? Not so much. Fortunately, there's a simple way to make sense of it.

Think of an AI Platform as a City

Every major AI platform is like a city. Most people simply use the roads, buildings, and public services without thinking much about how the city functions behind the scenes. Once you understand each component's responsibility, the different product names become much easier to remember.

The Knowledge Layer: The City's Map and Road Network

Every AI platform needs context. Think of this as the city's map and road network. It helps AI understand how information connects. Instead of seeing thousands of disconnected documents, AI begins to understand relationships between:

• People

• Teams

• Documents

• Projects

• Emails

• Customers

• Business systems

Most users never interact with this layer directly, but many AI features depend on it.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
Connectors + Retrieval	Connectors + Knowledge	Google Knowledge Graph + Vertex AI Search	Microsoft Graph	Teamwork Graph

Although the names differ, the purpose is the same: help AI understand relationships between people, information, and work.

AI Chat: The Tour Guide

Most people first experience AI through chat. Whether you're talking with ChatGPT, Claude, Gemini, Microsoft Copilot, or Rovo Chat, you're interacting with the city's tour guide. The guide doesn't own the information. Instead it:

• Consults the map

• Finds the best route

• Retrieves information

• Explains what it finds

The better the guide understands both your question and the city's map, the better the answer becomes.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
ChatGPT	Claude	Gemini	Microsoft 365 Copilot / Copilot Chat	Rovo Chat

AI Studios: The City Planning Office

Eventually organizations want AI to do more than answer questions. They want AI to:

• Build assistants

• Automate work

• Create knowledge hubs

• Connect systems

• Solve business-specific problems

That's where AI studios come in. Think of them as the city's planning office. This is where you decide:

• What should be built

• How work should flow

• What information AI can access

• Which actions AI can perform

  
  

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
GPTs	Projects	Gems	Copilot Studio	Rovo Studio

Different names. Same responsibility.

Low-Code App Builders: The Architect

Many AI platforms now let users build applications simply by describing what they want.

Think of these tools as architects. You might say:

Build an application that tracks equipment requests.

The architect produces the first blueprint. Simple applications may be ready to use immediately. More sophisticated applications often require additional development before they're production ready. AI makes building easier. It doesn't eliminate software engineering.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
GPT Builder	Artifacts	Vertex AI App Builder	Copilot Studio	App Builder

Developer Platforms: The Construction Crew

Eventually someone has to build the city. Developer platforms provide the infrastructure needed for production applications. They supply the building materials, security, deployment tools, and application services.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
OpenAI API	Anthropic API	Vertex AI	Azure AI Foundry	Forge

The architect creates the design. The construction crew builds it.

AI Coding Assistants: The Construction Foreman

Developers increasingly work alongside AI coding assistants. Think of them as construction foremen. They help teams:

• Write code

• Review code

• Debug problems

• Generate tests

• Explain unfamiliar code

• Improve documentation

The foreman doesn't replace the construction crew. It helps coordinate the work.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
Codex CLI	Claude Code	Gemini Code Assist	GitHub Copilot	Rovo Dev CLI

Other popular foremen include Cursor, Windsurf, Continue, and Aider.

Model Context Protocol (MCP): The City's Border Crossing

One term you'll hear much more often over the next few years is MCP. Think of MCP as the city's international border crossing. AI assistants increasingly need access to systems like:

• Slack

• GitHub

• Google Drive

• Microsoft 365

• Salesforce

• ServiceNow

• Jira

Rather than every application creating its own custom integration, MCP provides a standardized way for AI systems to securely communicate with external tools. The border crossing verifies:

• Identity

• Permissions

• Available tools

• Approved destinations

It doesn't contain the city. It simply manages how trusted visitors enter.

What the Major AI Platforms Call It

Platform	MCP Support
OpenAI	Supports MCP-compatible tools
Anthropic	Creator of MCP
Google	Expanding MCP support
Microsoft	Growing MCP adoption
Atlassian	Remote MCP Server

Command Line Interfaces (CLIs): The Workers' Radios

Many AI platforms also provide developer command-line tools. Rather than clicking menus, developers type instructions directly into a terminal. In our city analogy, a CLI is like giving construction workers radios instead of requiring them to return to city hall every time they need instructions. It's simply another way to interact with the same services.

What the Major AI Platforms Call It

OpenAI	Anthropic	Google	Microsoft	Atlassian
Codex CLI	Claude Code CLI	Gemini CLI	GitHub CLI & Azure CLI	Teamwork Graph CLI & Rovo Dev CLI

Bringing It All Together

City Analogy	Responsibility	Examples
🗺️ City Map	Connected organizational knowledge	Microsoft Graph, Teamwork Graph, Google Knowledge Graph
🧭 Tour Guide	AI Chat	ChatGPT, Claude, Gemini, Copilot, Rovo Chat
🏛️ Planning Office	AI Studio	GPTs, Projects, Gems, Copilot Studio, Rovo Studio
📐 Architect	Low-code Builder	GPT Builder, App Builder, Vertex AI App Builder
👷 Construction Crew	Developer Platform	OpenAI API, Vertex AI, Azure AI Foundry, Forge
👷‍♀️ Construction Foreman	AI Coding Assistant	GitHub Copilot, Claude Code, Gemini Code Assist, Rovo Dev CLI
🚧 Border Crossing	MCP	Model Context Protocol
📻 Workers' Radios	CLI	Codex CLI, Gemini CLI, Teamwork Graph CLI

Final Thoughts

One of the biggest sources of confusion in AI isn't the technology—it's the terminology. Every vendor has developed its own names for concepts that often perform very similar roles. Rather than memorizing dozens of product names, focus on the responsibility each component serves.

Once you understand the map, the tour guide, the planning office, the architect, the construction crew, the foreman, the border crossing, and the workers' radios, you'll be able to navigate almost any modern AI ecosystem with confidence. The names may change. The underlying responsibilities rarely do.