I build automation systems for businesses.
Systems that handle real conversations and workflows around the clock, without a human in the loop.
I've been doing this professionally for about 7 months now.
Early on I moved fast and patched things as they broke. That's fine for a prototype. It doesn't hold up with real clients.
What changed my approach wasn't a new model or a better prompt. It was building discipline into how I work with Claude Code, specifically through skills.
I've built 18 of them for my own stack so far.
▸Some skills handle how to write automation logic without breaking what's already working in production.
▸Some define how an AI conversation should move through a qualification flow.
▸Some lock in how edge cases get handled so I stop encountering the same failure twice.
▸Some generate consistent reports straight from raw data.
Each one is basically a decision I made after getting something wrong, written down so I don't have to make it again.
Before → spending the start of every session rebuilding context and still getting inconsistent output.
After → first attempt is usually ready for production.
The skills are one part of a bigger picture I've started thinking of as the harness.
It's not about the systems I build for clients. It's the controlled environment around how I build them.
Project structure, rules, what Claude Code is and isn't allowed to do, how context gets fed in before a session even starts.
The goal is that the quality of what comes out doesn't depend on how closely I'm paying attention on any given day.
And there will be a loop: taking the systems I've already shipped, running them against realistic scenarios, scoring how they actually hold up in my clients' context, and using that signal to improve what I build next.
Still learning a lot of this as I go!
If you're building AI systems for real use cases, I'd genuinely love to know how you're thinking about the reliability side.