What stood out to me here is the idea of orchestrating multiple coding agents across machines rather than treating AI coding as a single-agent workflow. Most discussions about AI-assisted development still assume one tool running locally, but the approach described here (task decomposition + parallel execution across machines) feels closer to how distributed build systems evolved.
The dependency-graph model is particularly interesting. If AI agents can operate on isolated git worktrees and resolve tasks in parallel, the bottleneck shifts from raw coding to how well the system can plan and coordinate tasks. In practice that probably means developers spend more time defining boundaries between tasks rather than writing every line themselves.
Another challenge I’ve noticed when experimenting with these tools is deciding which agent to use for which task. Different coding agents behave very differently depending on the type of work (refactoring, feature building, test generation, etc.). Having a runner that can dispatch tasks to different agents and machines could make that experimentation much easier.
I like the point about juniors needing to actually write code rather than relying entirely on AI tools.
One thing I’ve noticed while experimenting with AI coding tools is that they’re great at getting you unstuck or generating small pieces of code, but they often introduce unnecessary complexity when solving larger problems. If you don't already understand what "good" code should look like, it's really easy to accept something that technically works but is poorly structured.
In that sense, AI feels less like a replacement for programming and more like a productivity layer on top of it. The people who benefit most seem to be those who already understand how systems should be designed.
For beginners especially, I think the idea of using AI as a "TA" rather than a code generator is a really good mental model.
The dependency-graph model is particularly interesting. If AI agents can operate on isolated git worktrees and resolve tasks in parallel, the bottleneck shifts from raw coding to how well the system can plan and coordinate tasks. In practice that probably means developers spend more time defining boundaries between tasks rather than writing every line themselves.
Another challenge I’ve noticed when experimenting with these tools is deciding which agent to use for which task. Different coding agents behave very differently depending on the type of work (refactoring, feature building, test generation, etc.). Having a runner that can dispatch tasks to different agents and machines could make that experimentation much easier.
For anyone exploring the broader ecosystem of agentic coding tools, this overview was useful as well: https://prommer.net/en/tech/guides/best-ai-agentic-coding-to...
It compares several of the current tools and workflows that are emerging around multi-agent development.
Curious how people think this model scales once teams start coordinating dozens of agents simultaneously.