We live in an era of AI agents where every conference discussion these days is centered around slides showing robot icons linked by arrows, and the word 'agentic' pops up on every new product roadmap.
If you have only built demos on it, it looks pretty easy. All you need to do is give the model a goal, hand it a couple of API tools, and then you let it run. But that’s not the case when it comes to the real production part of it.
There is a severe gap between making an agent that books a flight in a Jupyter notebook and deploying one that handles travel for 40000 employees without overspending or leaking sensitive data. Tutorials often display only the happy part of things, and we do not realize how wide this gap is.
Let us briefly discuss five real-world engineering challenges that caught us off guard when taking agentic systems into production.
Challenge 1 — Agents Fail Because They Lack Context
