Agentic Engineering - Artur Barseghyan

Summary In the age of AI coding agents, LLMs are only as good as the context we give them. Vague prompts produce vague, unreliable code - so we need clear structure and rails. This talk shows a practical, structural approach to working with agents: how to turn any codebase into something agents can truly understand and work with reliably. You'll learn how to start a brand-new project from scratch, and how to adapt an existing one to become fully "agentic" with minimal effort. We cover agent harness tools, and share concrete tips on which free-tier and local models work best today. You'll see what a spec-driven workflow looks like. The result is more reliable code, fewer mistakes, less repetition, and documentation that actually stays correct. No hype - just hands-on techniques and workflows you can start using immediately. About Artur Barseghyan I like to talk about subjects, things I do, things I like/dislike or curious about. I don't like to talk about myself, so I'll keep it brief. I started to open-source in 2011. Currently working as Senior Software Engineer, shifting towards LLM Ops. I like generative AI and things you can do with it. When developing, I run most of the stuff on my laptop. Most of my free time is spent on kids/family. What's left is split between open-source, playing guitar, watching movies and listening to the music.