Nobody talks about how ugly production AI actually is.
The demo is beautiful. You wire up an LLM, feed it some data, it gives a perfect answer. You show your team. Everyone's excited.
Then you try to put it in production.
The LLM hallucinates on edge cases your test data didn't cover. The API times out under real load. The output format breaks the downstream system that's trying to parse it. A customer sends a query in a language you didn't test. The model provider changes their pricing and your unit economics collapse overnight.
You spend the first week building the agent. You spend the next eight weeks building everything around the agent — retry logic, fallback handling, output validation, logging, monitoring, cost tracking, user feedback loops, graceful degradation when the model is slow or wrong or down.
That's the work nobody shows in the demo.
I've shipped AI systems at enterprise scale and I can tell you: the model is maybe 20% of the effort. The other 80% is making it reliable, observable, and trustworthy enough that someone other than the person who built it will actually use it.
This is why most AI proof-of-concepts never make it to production. The gap between "it works on my laptop" and "it runs in our business" is an engineering problem, not an AI problem.
If you're evaluating AI for your business, don't ask "what can the model do?"
Ask "what happens when the model gets it wrong?" That answer tells you everything about whether the system will survive contact with reality.