alt Hacker NewsThere's lots of ways. You have to upskill through the stages IMO. Write code, write w/ agent, write w/ multi agents, write w/orchestrators.
My way is to just run a giant AI agent factory engine and make the agents full flow do everything. (plan long term, write prd, task, review).
Here's ~4000 commits in last month as an example, i have about ~10k ish including private/work stuff? https://github.com/portpowered/you-agent-factory/commits/mai...
The premise when you get to full automation generally is you go full industral engineering:
1. watch overall flow, improve process via continuous improvement
2. work via checklists and gates.
3. replace process with mechanisms as much as possible (code > agents)
4. optimal throughput is continual testing and iteration (CI, CD), coverage, full e2e tests, mock everything, general best practices really.
decent blog: https://openai.com/index/harness-engineering/
general points:
- build lots of linters
- document literally everything (arch, prd, best practices in repo)
- too many agents at the same time makes lots of code conflicts, so need to consider architecture of code how to maximize concurrency.
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Have you been able to build anything substantial with AI factory itself? I have done some of these experiments myself on these sort of things and found they ended up often being less effective than using the latest tools in harnesses like claude code.
But curious if you've found it to be a big unlock. I have been doing some of this industrial engineering myself.
Genuinely curious - in your case, where do the requirements for what needs to be built come from?
In every project I've touched, business requirements are always the bottleneck - so I've never been able to wrap my head around what kind of requirements can be fed into a setup like this at high enough volume to justify it.