Question
What is blameless post-mortem?
Quick Answer
When an agent fails to fire or produces bad results you learn how to improve it.
Example: You built an agent: 'When I feel overwhelmed at work, pause and write down the three most important tasks.' Two weeks in, you notice you haven't triggered it once. That failure is diagnostic data. Investigation reveals the trigger ('feel overwhelmed') is too vague — by the time you recognize the feeling, you're already deep in reactive mode. You revise the trigger to 'When I open my laptop and have more than five unread Slack threads.' Now it fires reliably. The failure didn't prove the agent was bad. It proved the trigger specification needed debugging.
This concept is part of Agent Fundamentals in the How to Think curriculum.
Learn more in these lessons
- Agent failure is learning data
- Post-decision review
After a decision plays out review whether your framework served you well.
- The blame instinct prevents learning
Focusing on who caused an error prevents understanding why it happened.
- Feedback from errors is the most valuable feedback
Errors teach you more about your systems than successes do.