You've built the demo. It runs clean, the stakeholders are impressed, and someone in the room says "let's ship this." Then reality hits. The model starts hallucinating on edge cases. Token costs spiral. Your clean prototype data doesn't look anything like what real users throw at it. The agentic workflow that looked elegant in the notebook turns into an infinite loop in staging. This is the gap almost no one talks about: the massive, messy distance between a working prototype and a production-grade AI application. I had a deep conversation with Manav Goyal, Principal Technical Consultant at Geekians, about exactly this , what breaks, what to build differently, and how to think about AI systems that actually hold up under real-world pressure. (You can watch the full discussion ( https://www.youtube.com/watch?v=PrIK6Z6TA_I ) Here's what I took away. The Prototype vs. Production Mindset Shift The fundamentals are genuinely different between the two phases , and confusing them is where most teams go wrong.…