My Way Toward Agentic Coding

Fri, 16 Jan 2026 09:47:00 +0000
Machine Learning, AI, Agentic Coding

My initial interest in AI appeared pretty early. I remember participating in a conference in Belgrade in 2016, where there was a lot of talk about chatbots and how they would change the world.

That's also where I learned about IBM's Watson - one of the first AI tools, though back then we didn't call them that yet.

First steps

I remember my first attempts to use Watson to connect it with our company bot to improve communication. These were clumsy attempts to adapt AI to what I wanted to achieve, and not being an ML engineer, I simply didn't understand many concepts. It wasn't a system for everyone - you needed a certain level of ML knowledge to use it.

I remember being amazed by AI's capabilities when ChatGPT was released. I guess I wasn't the only one who was fascinated by how powerful AI could be. Comparing the experience of using Watson to ChatGPT's capabilities was like switching from a Maluch to a Polonez (that's a Polish joke - both are old Polish cars, but the Polonez was better).

ChatGPT just worked - you asked a question and got an answer, without any complicated programming tricks - a tool for the people!

I immediately connected our company bot to improve its "intelligence". And this integration worked perfectly for casual conversations. You could ask questions directly from our Slack and have a discussion with the bot in a thread, which is still happening today.

However, I was missing some contextual knowledge about what the bot itself could do - and it had several functions supporting our company's work. Attempts to customize ChatGPT to better understand our company's specifics were just as difficult as earlier with Watson.

But ChatGPT's capabilities related to generating code (back then still in the chat window), knowledge related to explaining code complexities, especially in a language I didn't know (I was learning TypeScript at the time), amazed me.

Of course, I quickly realized that AI makes mistakes or, as it's now called, hallucinates. The generated code, after pasting it into the IDE editor, turned out to be faulty, wouldn't compile, etc. So I would go back to the chat window and painstakingly try to get the correct result, copying successive versions of code into the editor and checking if they worked.

It was a phenomenon for me to discover that ChatGPT could generate Python code and run it. This allowed me to speed up work by generating a solution in Python, testing it on ChatGPT's side, and then converting the solution to TypeScript through AI - it worked rather poorly, as you can imagine. But it allowed me to experiment faster with what works and what doesn't, and if I remember correctly, this capability still exists.

AI-assisted coding

Perhaps the biggest excitement came when Copilot released a plugin for IntelliJ IDEA, which allowed direct integration with AI, whether through chat or through code completion. The productivity boost was mind-blowing, like good wine.

With a few...