What does it mean that test agents before deploying?

Run through scenarios mentally or in low-stakes situations before relying on a new agent.

Example: You build a morning routine agent — a sequence of cues, decisions, and actions designed to get you from alarm to deep work in 45 minutes. Instead of deploying it tomorrow when you have a 9 AM client call, you test it on Saturday when nothing depends on it. The agent fails at step three: your coffee ritual takes 20 minutes, not five. You adjust. Monday, the revised agent runs clean. If you had tested in production, you would have been late to the call and blamed the system.

Try this: Pick one agent (behavioral routine, decision rule, or AI workflow) you want to deploy. Before using it in a real situation, run a pre-mortem: imagine it is six weeks from now and the agent has completely failed. Write down three specific reasons it failed. Then run the agent in a low-stakes scenario — a practice day, a test dataset, a hypothetical decision. Compare what actually broke against your pre-mortem predictions. Adjust the agent before deploying it where the stakes are real.