Good AI systems aren't assembled from trending libraries. They're shaped with intent — each layer deliberate, each decision load-bearing.
A script runs. An agent decides. The difference is architecture, not prompt engineering. Five years across QA, backend, ML, and GenAI taught the same lesson every time: frameworks aren't the point. Intent is. I build systems where every component earns its place.
Every agent answer must trace back to retrieved context. If it can't cite the source, it doesn't ship. RAG isn't a feature — it's the foundation.
Agents need room to reason, but within hard boundaries. State machines enforce the rails. The LLM handles the thinking inside them.
Map the problem. What data exists, where it flows, what breaks. No code until the constraints are clear.
Tight architecture. Every component has a reason. If it doesn't serve the system, it doesn't exist.
Load test, observe, break it yourself. Production readiness isn't a checkbox — it's what happens at 3 AM when nobody's watching.
A GenAI engineer who's shipped Unity games debugs latency differently. One who's written OCR pipelines thinks about document intake differently. One who's tuned models from scratch picks providers differently.
Specialization is how you get good. Range is how you stay useful. The forge keeps both.