A demo that works once is not the same as an automation you can trust. Language models can state wrong answers with confidence, a model reading outside text can be tricked into following instructions hidden in it, and any step that calls another service can fail on a bad day. Reliability is the work of expecting these problems before they reach a customer or a real account.
You will start with why models hallucinate and what that means for the tasks you give them. Then you will look at prompt injection and the idea of layered defenses, where checks sit before and after the model rather than relying on the model alone. On the workflow side, error handling means retrying temporary failures, catching the rest in one place, and being told when something breaks instead of finding out from a user.
The last piece is human-in-the-loop approval: letting an agent propose an action such as sending an email, and requiring a person to approve it first. Put review where the consequences are largest. Watch the lessons in order, then continue on to Deploying & Monitoring.
IBM Technology explains why language models produce confident but wrong answers and what can reduce the problem, which tells you how much to trust a model's output inside a workflow.
IBM's Jeff Crume covers attacks on language model applications, including prompt injection, and a layered defense with checks placed between the user and the model.
n8n's Liam McGarrigle builds a Gmail and Calendar assistant, limits which fields the model controls, and adds an approval step that intercepts sensitive actions. It is a long workshop, so watch it in parts and build along.