One of the more interesting conversations I sat in on recently wasn’t about building AI workflows. It was about maintaining them.
Ovidius AI was discussing a challenge I think a lot of AI implementation firms run into:
How do you maintain delivery quality as AI systems become more complex?
The workflows themselves weren’t the issue. The team already builds across tools like N8N, Next.js, Supabase, and multiple LLMs.
The challenge was operational:
regression testing,
edge cases,
inconsistent AI outputs,
error categorization,
and building QA processes that don’t cost as much as the projects themselves.
That led to an interesting discussion with Ben Fellows and the team @ LoopQA around what QA for AI systems actually looks like in practice.
Not just “does this app work,” but:
- how to design reusable test harnesses
- where AI-generated test cases fit
- how to think about deterministic vs. generative outputs
- and how teams build trust and operational visibility around AI-driven workflows
It feels like a lot of companies are entering a new phase of AI adoption. The challenge is no longer just building workflows. It’s figuring out how to scale, validate, and trust them in production.
Thanks to Jason / Ben & team for the practical discussion.