Julian Neagu

The tiered approach is the part I like most. Not every request needs a big model. Using cheaper checks first makes AI systems much more practical.

I’ve seen solo builders struggle when they split web and mobile too early. One repo with clear separation usually keeps things moving much faster.

I’ve seen this a lot with AI coding tools. If the library is newer or less popular, they just push you toward the old options. Reading docs still saves time.

The part about failure recovery stood out. Most agent demos look fine until something breaks. That's where the real engineering starts.

This is the kind of stuff you only learn after running AI in production. The hidden token costs part is real. Measuring beats guessing every time.

The model is only one piece. The hard part is making all the parts work together in the real world. A lot of AI projects hit this wall after the demo stage.

The audit trail point is spot on. I think many teams focus on making agents smarter but forget they need to understand what happened when things go wrong.

The docs-first approach is something I’ve seen make a huge difference with AI projects. If the context only lives in chat, things get messy fast. Keeping decisions somewhere stable makes AI way more useful.

AI memory feels like the next big security layer. We used to worry about bad inputs in a single request, now one bad write can follow the agent for weeks. Provenance and expiry should become normal.

I like the AI examples here. Agent traces and workflow steps are places where these old concepts still show up in newer systems.