How to Safely Execute LLM Commands in Production Systems

LLM agents are becoming operational interfaces. This is part 2 of our ongoing series on safely integrating LLMs with production backends. Before diving in, you might want to read the previous part. They summarize tickets, inspect logs, propose remediation steps, and increasingly trigger backend actions. That is exactly where the real risk begins. In production systems, the question is not whether a model can generate commands. It is whether those commands are executed through a deterministic boundary that your application can validate, reject, and audit.