The AI Lego Stack: How Modern AI Tools Fit Together
Here's a conversation that happens constantly right now.
The AI Lego Stack
Click each layer to explore
The AI Lego Stack: How Modern AI Tools Fit Together
Part 3 of the "Build with AI" series
Here's a conversation that happens constantly right now.
Someone decides they want to "use AI" for their business. They open ChatGPT. They try a few things. Some of it works, some doesn't. They hear about Claude, switch over. Then someone mentions Cursor for coding, so they install that. Then a colleague says they should be using n8n for automation. Then there's Midjourney for images. Then Make.com. Then someone posts about a new tool on LinkedIn and suddenly there are seventeen browser tabs open and a creeping sense that the whole thing is too complicated to actually figure out.
Sound familiar?
The problem isn't the tools. The problem is that nobody explained how they fit together.
Once you see the structure — what each layer does, how they connect, what to pick for what job — the whole thing stops feeling overwhelming and starts feeling like a set of building blocks. Lego, not chaos.
The three layers of the modern AI stack
Think of every AI-powered solution as being built from three distinct layers, stacked on top of each other. Each layer has a job. Each depends on the one below it.
┌─────────────────────────────────┐
│ LAYER 3: INTERFACE │ ← How humans interact with the solution
├─────────────────────────────────┤
│ LAYER 2: ORCHESTRATION │ ← How tasks are coordinated and automated
├─────────────────────────────────┤
│ LAYER 1: THE MODEL │ ← The AI brain doing the actual thinking
└─────────────────────────────────┘
Let's walk through each one.
Layer 1: The model — the brain
This is the AI itself. The large language model (LLM) that actually understands language, reasons, generates text, writes code, summarizes documents, answers questions.
The major players you need to know:
OpenAI — GPT-5.2 The current flagship, built for coding and agentic tasks across industries. GPT-5 mini and GPT-5 nano offer lighter, faster, and cheaper alternatives for well-defined tasks. Accessible through ChatGPT or the API.
Anthropic — Claude Opus 4.6, Claude Sonnet 4.6 Claude Opus 4.6 is Anthropic's most capable model — exceptional at long documents, nuanced writing, complex instruction-following, and extended reasoning, with a 1 million token context window. Claude Sonnet 4.6 is the everyday sweet spot: fast, highly capable, and cost-efficient for most use cases. Available through claude.ai or the API.
Google — Gemini 3.1 Pro / Gemini 2.5 Pro Gemini 3.1 Pro is Google's newest and most capable model (currently in preview), built for complex problem-solving and agentic tasks. Gemini 2.5 Pro is the current stable flagship — strong reasoning, very long context window, and deep integration with Google's ecosystem (Docs, Sheets, Gmail). Strong multimodal capabilities — handles text, images, audio, and video natively.
Meta — Llama 4 (open-source) Meta's latest open-source model family. Free, can be run locally or self-hosted. Important if you need data privacy, cost control, or customization that proprietary models don't allow. Llama 4 has closed the gap significantly with the leading proprietary models.
xAI — Grok 4.20 xAI's newest flagship, with industry-leading speed and agentic tool-calling capabilities. Real-time access to X (Twitter) data via X Search is its distinctive edge — useful for social listening, trend analysis, and anything where live public conversation is the signal.
The key insight: you don't need to pick one and stick with it forever. Different models have different strengths. Many builders use different models for different tasks — Claude Opus 4.6 for deep reasoning and long documents, GPT-5.2 for coding and agentic tasks, Gemini 3.1 Pro for Google-ecosystem workflows. The model is interchangeable. Your prompts, your logic, your workflows — those are the assets worth investing in.
Layer 2: Orchestration — the coordinator
A model on its own is powerful but limited. It answers one question at a time. It can't automatically check your email every morning, pull data from your CRM, process it, and send a summary to Slack. It can't run a multi-step workflow without someone sitting there typing each prompt manually.
That's what the orchestration layer does. It coordinates: triggering the AI at the right time, feeding it the right data, connecting it to other tools, and acting on the output automatically.
This is the layer most people skip — and it's where the real leverage lives.
n8n — A visual workflow automation tool. You connect nodes (triggers, actions, AI calls) on a canvas. When X happens, do Y, then call AI, then do Z. Open-source, self-hostable, excellent for builders who want control. This is what powers most serious AI automation workflows.
Make.com — Similar to n8n but more beginner-friendly, cloud-based, with a large library of pre-built connectors. If you want to get something running fast without much technical setup, Make is your starting point.
LangChain / LlamaIndex — Developer-focused frameworks for building more complex AI pipelines in code. If you're working with a developer or learning to code, these give you fine-grained control over how AI interacts with data and tools.
AI Agents — Worth mentioning separately because they're increasingly their own orchestration layer. An agent is an AI that can plan a sequence of steps, use tools, and complete a goal with minimal human intervention — essentially a workflow that figures out its own steps. We'll dedicate a full post to agents later in this series.
Layer 3: Interface — the front door
This is what the user actually sees and touches. The interface is how humans interact with whatever you've built.
Chat interfaces — The simplest. A text box, a response. Claude.ai, ChatGPT, custom chatbots built on top of an API. Good for open-ended interaction.
Web apps and dashboards — Built with tools like Bolt, Lovable, Vercel v0, or traditional development. A proper UI that feels like a real product, not a chat window.
Embedded AI — AI built into an existing tool or workflow the user already uses. A button in Google Docs that summarizes the document. A Slack command that runs a report. An email that triggers an automated analysis. The user never thinks "I'm using AI" — they just use the tool.
Voice interfaces — Growing fast. AI that listens and responds. Customer service bots, voice assistants, meeting transcription and summarization tools.
The interface is often the last thing to build, but it's the first thing users judge. A brilliant AI workflow with a clunky interface will be abandoned. A simple workflow with a clean, intuitive interface will be used every day. Don't underestimate this layer.
How the layers connect: a real example
Let's make this concrete. Imagine you want to build a tool that monitors your competitors' websites, summarizes what changed, and sends you a weekly digest.
Layer 1 (Model): Claude Sonnet 4.6 or GPT-5.2 — reads the competitor content and generates a clear, concise summary of what's new and why it matters.
Layer 2 (Orchestration): n8n — runs every Monday morning, fetches the competitor URLs, passes the content to the AI model, formats the output, and sends it to your email or Slack.
Layer 3 (Interface): Your email inbox or a Slack channel — that's where the digest lands. You don't need a fancy UI; the interface is wherever you already spend your time.
Total coding required to build this: zero, if you use n8n's visual interface and a pre-built email connector. The logic is yours. The AI does the reading and summarizing. The orchestration does the scheduling and routing. The interface is already built — it's your inbox.
This is the Lego model in action. Each piece has a job. You assemble them. You don't build the bricks from scratch.
How to pick your stack
You don't need to master all of this at once. Here's a practical starting framework based on where you are:
If you're just getting started:
- Model: ChatGPT or Claude (use the chat interface directly)
- Orchestration: none yet — do things manually to understand the workflow first
- Interface: the chat window
If you want to automate something:
- Model: Claude Sonnet 4.6 or GPT-5.2 via API
- Orchestration: Make.com (easier) or n8n (more powerful)
- Interface: email, Slack, or a simple web form
If you want to build a real product:
- Model: Claude Opus 4.6 or GPT-5.2 via API, potentially multiple models for different tasks
- Orchestration: n8n or LangChain, with AI agents for complex workflows
- Interface: Bolt, Lovable, or v0 for rapid UI building without deep coding
Start with the simplest version that does the job. Add layers as the need becomes clear. Most people over-engineer the stack before they understand the problem. Understand the problem first (back to Post 1), then choose the stack.
The mistake most people make
They pick tools before they understand the problem.
They hear about n8n and build an automation before knowing what they want to automate. They set up LangChain before knowing if they need that level of complexity. They choose a model based on what's trending, not what fits the task.
The stack should follow the use case, not the other way around.
Before choosing any tool, ask: What is the job to be done? Where does AI add value in that job? How will the result reach the person who needs it? Those three questions map directly to Layer 1, Layer 2, and Layer 3. Answer them first. Then choose your Lego pieces.
What to remember from this post
Every AI solution has three layers — Model, Orchestration, Interface — and understanding which layer you're working in cuts through the tool overwhelm immediately. A model alone answers questions; orchestration makes it act automatically; interface makes it usable. You need all three for anything real.
The orchestration layer is where most of the leverage lives, and most people skip it. Connecting AI to triggers, data sources, and actions via tools like n8n or Make.com is what turns a chat interaction into a working system. Don't over-commit to one model — your prompts, logic, and workflows are the real assets. The model is a replaceable component. Start simple, add layers as needed. Complexity is a cost, not a feature.
Want the full framework?
This post covers the architecture. The AI Development Guide by Jaehee Song goes deeper into how each layer works in practice — with real examples of stacks built for specific use cases, from solo operators to enterprise workflows.
📱 Apple Books ▶️ Google Play Books 🌐 All Platforms (Books2Read)
Next in the series: "Your Data is the Real Bottleneck" — why the quality of what you feed AI matters more than which AI you use, and how to know if your data is actually ready.