Originally published on tamiz.pro.
The demo works beautifully. The agent reads the inbox, writes a draft, calls the API, and updates the database — all in one fluid, 90-second recording where the LLM "just knows" what to do. Then someone asks you to ship it. That's when the real work begins.
Every engineering team watching the AI agent wave is now under pressure to deliver. But the path from a polished demo to a production system is not a linear scaling exercise. It's a series of failure modes that no prompt can save you from. Below are the patterns I've seen break production deployments — and the engineering disciplines required to fix them.
The Determinism Illusion
LLMs are non-deterministic by design. A demo succeeds because the recording was made on a happy path. Production doesn't care about happy paths — it only cares about edge cases, which are exponentially more numerous than the scenarios you tested in the demo.
