Hey HN, author here.
I spent last week in a deep-dive experiment to see how far I could push modern agentic workflows on a greenfield project. I wanted to move past simple code generation and see if I could build a system where I was orchestrating a team of agents to build a full application.
The results were pretty wild (~800 commits, 100+ PRs, and a functioning app we use internally at my company), but the most interesting part was the playbook of rules I had to develop to make it work. The post covers the 8 rules I learned, from managing the AI's context window with sub-agents and manual checkpoints, to creating autonomous test loops, to why I had to become ruthless about restarting failed runs.
A few quick notes to preempt questions:
Tech Stack: The core of this was Claude Code, a custom parallelization script, and open-source MCPs like Serena.
Cost: The token cost was significant (~$6k). This was an experiment to push the limits, not to optimize for cost efficiency... yet.
Effort: This was not a standard 40-hour week. It was an intense, "in the hole" sprint with a very high cognitive load.
I’m convinced the role of an engineer is shifting from a hands-on coder to an architect of these intelligent systems. I’m curious to hear how others are approaching this. What workflows or tools for managing agents have you found to be effective?
I spent last week in a deep-dive experiment to see how far I could push modern agentic workflows on a greenfield project. I wanted to move past simple code generation and see if I could build a system where I was orchestrating a team of agents to build a full application.
The results were pretty wild (~800 commits, 100+ PRs, and a functioning app we use internally at my company), but the most interesting part was the playbook of rules I had to develop to make it work. The post covers the 8 rules I learned, from managing the AI's context window with sub-agents and manual checkpoints, to creating autonomous test loops, to why I had to become ruthless about restarting failed runs.
A few quick notes to preempt questions:
Tech Stack: The core of this was Claude Code, a custom parallelization script, and open-source MCPs like Serena.
Cost: The token cost was significant (~$6k). This was an experiment to push the limits, not to optimize for cost efficiency... yet.
Effort: This was not a standard 40-hour week. It was an intense, "in the hole" sprint with a very high cognitive load.
I’m convinced the role of an engineer is shifting from a hands-on coder to an architect of these intelligent systems. I’m curious to hear how others are approaching this. What workflows or tools for managing agents have you found to be effective?