ModernApps¶

AI Agent Application Demo: Putting a Brain Inside Your App

Source code: github.com/pkhamdee/coffee-agent

There's a quiet revolution happening in how we write software. For decades, we've built applications the same way: write a function, call the next function, handle each case with an if statement, repeat. The logic is explicit, deterministic, and completely predictable — a flowchart carved into code.

That model still works. But it has a hard ceiling.

When a user wants to do something that doesn't fit neatly into your flowchart — when they say something ambiguous, change their mind mid-conversation, or combine requests in ways you didn't anticipate — the rigid-logic app breaks down. You end up writing more and more special-case handling until the code becomes unmaintainable.

AI agents flip this model. Instead of programming every decision upfront, you give your application a reasoning engine — a brain — and let it figure out what to do. The application stops being a flowchart and starts being a collaborator.

This post walks through a real, runnable example: a coffee shop ordering chatbot called Coffee Agent. It's a full-stack app built with NestJS, React, LangGraph, and a local LLM running on Ollama. By the time you finish reading, you'll understand exactly what an agent is, why this architecture is powerful, and how to build one yourself.

Tech Stack of a Modern AI App in 2026: The Complete Layer-by-Layer Guide

Everyone wants to build an AI app. Most people start with the same two lines:

import openai
response = openai.chat.completions.create(...)

That works for a demo. It does not work for a product. The moment you try to serve real users, you run into a wall of unanswered questions: Where does your data live? How do you keep the model's knowledge fresh? How do you know when it starts giving bad answers? How do you deploy it without rewriting everything from scratch every time the model changes?

A production AI application in 2026 is not a Python script. It's a 10-layer system — each layer solving a specific class of problem, each with its own ecosystem of tools.

This post is the map. We'll walk every layer from the ground up: what problem it solves, which tools the industry has standardized on, and how the layers connect. By the end, you'll be able to look at any real-world AI product and name exactly what's running inside it.

9 Essential Components of a Production Microservice App

So you've built a microservice. It works on your laptop. Now your manager says "take it to production" — and suddenly you realize a working service and a production-ready service are two very different things.

This guide walks through all 9 essential components that every production microservice system must have, using the architecture diagram below as our map. By the end, you'll understand not just what each component is, but why it exists, how it works, and how to implement it in real code.