Here's what I wish someone told me early on: your agent will call the wrong tool. Your RAG pipeline will retrieve confidently wrong context. Your deployment will timeout the one time your CEO is watching. I know because I've shipped all of those bugs — and then fixed them. I've built multi-agent systems from blank repos to production, designed hybrid routers that know when to think and when to just regex, and wired human-in-the-loop flows because I learned early — never let an agent guess when money is on the line. Before going deep on agentic AI, I spent years building ML systems at enterprise scale — the kind where "it works on my machine" isn't an answer. My master's research taught me exactly how LLMs fail, which turns out to be the most useful thing you can know when building with them. I do 1:1 mentorship and consulting for people building real AI products. Not theory. Not "just use LangChain." The actual decisions that matter — when to add an agent vs. a simple chain, how to evaluate outputs without losing your mind, why your retrieval sucks and what to do about it. If you're building something serious, I'll help you skip the mistakes I already made.