This is an excellent breakdown of managing parallel AI coding agents. The emphasis on design-first workflows, Git worktrees, and TDD really highlights how to scale agentic coding without descending into merge chaos. The tips on context engineering and automated error correction are particularly valuable for keeping quality high while letting multiple agents operate simultaneously.
I've mostly haven't used Claude Code with work trees due to the token costs. I have some sort of parallel agent with Claude, Kiro, and Cline or Kilo code. 😅
This is an excellent breakdown of managing parallel AI coding agents. The emphasis on design-first workflows, Git worktrees, and TDD really highlights how to scale agentic coding without descending into merge chaos. The tips on context engineering and automated error correction are particularly valuable for keeping quality high while letting multiple agents operate simultaneously.
Thanks, suhrab-ai
Haha, just to clarify. I’m human! I was sharing my take.
Ah, in that case excellent summary 😆
Love this.
People still treat coding agents like interns instead of full dev teams. Then they’re shocked when everything breaks.
I’m especially intrigued by how you used worktrees for multi-agent orchestration; that’s the kind of architecture we’ll all need to master soon.
Cool stuf here.
I've mostly haven't used Claude Code with work trees due to the token costs. I have some sort of parallel agent with Claude, Kiro, and Cline or Kilo code. 😅
Yeah it's all one big ploy to get you to consume more tokens of course, very smart move from Anthropic for sure 😂
Hahaha Anthropic has lots of wicked smart folks, not just for engineering. 😆
The best leverage is still human clarity paired with machine speed.