From Prompt Loops to Systems: Hosting AI Agents in Production

An agent can reason well and still fail badly. Most teams do not notice this during early experiments because nothing is under pressure yet. The model calls tools, answers questions, and produces outputs that look correct. From the outside, the system works. The problems surface later, once the agent is expected to run continuously instead of intermittently. Restarts become normal, context has to survive across runs, external services are often involved, and their actions are not always closely monitored. That is where the difference shows. At that point, outcomes depend far less on how the agent reasons and far more on how it is hosted, because hosting determines what happens when execution is interrupted, state disappears, or permissions suddenly block an action.