Lead Architect Carlos Kelkboom shares his rules for coding agents and how great engineers architect software with AI. One of the great ones is: he throws away generated code the moment he doesn't understand it. He breaks down why coding agents fail at iterative changes, why his main agent never touches files, and how he scaffolds projects for one-shot execution.
In this video, we cover:
Why AI coding agents struggle with iterative changes (SlopCodeBench) and excel at one-shot execution
How to manually scaffold code, types, and folder boundaries before letting sub-agents run
Designing a distributed Markdown and indexed-search graph harness for progressive context disclosure
Using Git worktrees to run parallel coding agents and merge the best implementations
Why dynamic memory and the heap need to disappear in favor of deterministic programming languages
How AI bubbles and solo coding impact junior engineers and team collaboration
This episode is for software engineers, tech leads, and system architects who want to move beyond "vibe coding" and build maintainable, deterministic production software with AI agents.
Timestamps: 00:00:00 - Why Carlos Throws Away Working AI Code 00:00:53 - Why Pull Requests and Team Collaboration Are Dying 00:03:50 - The Trap of Starting Projects You Never Finish 00:06:06 - Escaping the Solo AI Developer Bubble 00:11:54 - How to Scaffold Code Before Using AI Agents 00:15:54 - Learning New Programming Languages With AI Agents 00:21:14 - Why We Must Get Rid of the Heap 00:28:32 - Building a Shared Language Across Engineering Teams 00:31:31 - Vibe Coding vs Deep Software Engineering 00:40:12 - Code Is Cheap: Git Worktrees for Parallel Agents 00:41:45 - How Deep to Scaffold Before the Agent Runs 00:45:09 - Democratizing Software, Missing Juniors, and the AI Bubble 00:53:33 - Designing a Graph Harness for AI Agent Context 00:58:18 - Why His Main Agent Never Touches Files 01:03:07 - Throw Away Code You Don't Understand 01:06:25 - Rethinking Code Reviews and Mutation Testing for AI
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