Concrete practices that made AI coding work

AI Assisted Coding: Stop Building Features, Start Building Systems with AI

What separates vibe coding from truly effective AI-assisted development? In this episode, Adam Bilišič shares his framework for mastering AI-augmented coding, walking through five distinct levels that take developers from basic prompting to building autonomous multi-agent systems.

Vibe Coding vs AI-Augmented Coding: A Critical Distinction

"The person who is actually creating the app doesn't have to have in-depth overview or understanding of how the app works in the background. They're essentially a manual tester of their own application, but they don't know how the data structure is, what are the best practices, or the security aspects."

Adam draws a clear line between vibe coding and AI-augmented coding. Vibe coding allows non-developers to create functional applications without understanding the underlying architecture—useful for product owners to create visual prototypes or help clients visualize their ideas.

AI-augmented coding, however, is what professional software engineers need to master: using AI tools while maintaining full understanding of the system's architecture, security implications, and best practices. The key difference is that augmented coding lets you delegate repetitive work while retaining deep knowledge of what's happening under the hood.

From Building Features to Building Systems

"When you start building systems, instead of thinking 'how can I solve this feature,' you are thinking 'how can I create either a skill, command, sub-agent, or other things which these tools offer, to then do this thing consistently again and again without repetition.'"

The fundamental mindset shift in AI-augmented coding is moving from feature-level thinking to systems-level thinking. Rather than treating each task as a one-off prompt, experienced practitioners capture their thinking process into reusable recipes. This includes documenting how to refactor specific components, creating templates for common patterns, and building skills that encode your decision-making process. The goal is translating your coding practices into something the AI can repeatedly execute for any new feature.

Context Management: The Critical Skill For Working With AI

"People have this tendency to install everything they see on Reddit. They never check what is then loaded within the context just when they open the coding agent. You can check it, and suddenly you see 40 or 50% of your context is taken just by MCPs, and you didn't do anything yet."

One of the most overlooked aspects of AI-assisted coding is context management. Adam reveals that many developers unknowingly fill their context window with MCP (Model Context Protocol) tools they don't need for the current task. The solution is strategic use of sub-agents: when your orchestrator calls a front-end sub-agent, it gets access to Playwright for browser testing, while your backend agent doesn't need that context overhead. Understanding how to allocate context across specialized agents dramatically improves results.

The Five Levels of AI-Augmented Coding

"If you didn't catch up or change your opinion in the last 2-3 years, I would say we are getting to the point where it will be kind of last chance to do so, because the technology is evolving so fast."

Adam outlines a progression from beginner to expert:

• Level 1 - Master of Prompts: Learning to write effective prompts, but constantly repeating context about architecture and preferences

• Level 2 - Configuration Expert: Using files like .cursorrules or CLAUDE.md to codify rules the agent should always follow

• Level 3 - Context Master: Understanding how to manage context efficiently, using MCPs strategically, creating markdown files for reusable information

• Level 4 - Automation Master: Creating custom commands, skills, and sub-agents to automate repetitive workflows

• Level 5 - The Orchestrator: Building systems where a main orchestrator delegates to specialized sub-agents, each running in their own context window

The Power of Specialized Sub-Agents

"The sub-agent runs in his own context window, so it's not polluted by whatever the orchestrator was doing. The orchestrator needs to give him enough information so it can do its work."

At the highest level, developers create virtual teams of specialized agents. The orchestrator understands which sub-agent to call for front-end work, which for backend, and which for testing. Each agent operates in a clean context, focused on its specific domain. When the tester finds issues, it reports back to the orchestrator, which can spin up the appropriate agent to fix problems. This creates a self-correcting development loop that dramatically increases throughput.

In this episode, we refer to the Claude Code subreddit and IndyDevDan's YouTube channel for learning resources.

About Adam Bilišič

Adam Bilišič is a former CTO of a Swiss company with over 12 years of professional experience in software development, primarily working with Swiss clients. He is now the CEO of NodeonLabs, where he focuses on building AI-powered solutions and educating companies on how to effectively use AI tools, coding agents, and how to build their own custom agents.

You can connect with Adam Bilišič on LinkedIn and learn more at nodeonlabs.com. Download his free guide on the five levels of AI-augmented coding at nodeonlabs.com/ai-trainings/ai-augmented-coding#free-guide.