Why does pre-mortem technique fail?

The most common reason pre-mortem technique fails: Skipping the test because you are excited about the new agent and confident it will work. Overconfidence is the specific failure mode Klein's pre-mortem was designed to counter. You deploy untested, something breaks under real conditions, and instead of learning from a controlled failure you are doing damage control. Worse, you blame the agent concept rather than the missing test step — and abandon a system that would have worked with one iteration.

The fix: 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.

The underlying principle is straightforward: Run through scenarios mentally or in low-stakes situations before relying on a new agent.