A lightweight Azure backend for my AI agent

Over the past few weeks, I’ve been exploring different ways to power a personal AI agent for my blog, one that can answer questions about me, my background, and my work using context I provide. I wanted a simple, secure, and cost-effective backend that I fully control and can iterate on fast.

n8n is a powerful open-source automation tool that’s perfect for wiring together APIs and logic without having to spin up tons of infrastructure.

The first time I connected Visual Studio Code with GitHub Copilot, I expected party tricks. I got something closer to a new way of working. It wasn’t that code appeared by magic. It was that the little frictions, like the boilerplate, the glue code, the tests I knew I should write but kept postponing, stopped dominating my day. VS Code had already been my editor of choice for its ergonomics and extensibility; Copilot turned that editor into a cockpit where intention became motion. This post is my field report: what I actually run, how I prompt, where it saves time, and where I still slow down and think.

Imagine this: you work at a company, and you have a clear idea for a web app, something like a custom expense tracking tool that fits exactly the way your team works. You’ve tried off-the-shelf products, but they always miss the mark. The alternative, building your own application, is usually time-consuming, expensive, and requires getting developers involved early on. But what if you could prototype your idea, adjust the UI, change the data model, and publish it, all without writing a single line of code?

AI is no longer an abstract promise of the future. It’s here, embedded into enterprise workflows, products, and decision-making processes. From Microsoft Copilot to ChatGPT and domain-specific assistants, businesses are adopting AI at an unprecedented pace. But too often, “AI” is used as a catch-all term for a wide range of technologies. To lead the next transformation wave, organizations must move beyond generic AI adoption and toward agentic AI, a more autonomous, goal-driven form of AI that’s ready to take on real work.

In today’s AI era, you’ve likely interacted with Microsoft Copilot, ChatGPT, or Claude.ai, tools powered by advanced Large Language Models (LLMs). These models excel at understanding and generating human-like text based on vast amounts of training data. However, while LLMs are impressive at reasoning and answering general questions, they fall short when it comes to performing real-world tasks or retrieving live, domain-specific information.

This is where the Model Context Protocol (MCP) comes in.

The logistics industry constantly seeks efficiency in routing vehicles to deliver goods. This is where the Vehicle Routing Problem (VRP) and Pickup and Delivery Problems (PDP) come into the picture as both are sophisticated extensions of the classic Traveling Salesperson Problem (TSP). The TSP poses a simple yet challenging question: “What is the most efficient route that visits each destination once and returns to the starting point?” This problem is not just academic; it has practical applications in logistics, where optimizing routes can lead to substantial savings in time and costs.