Resilience in Action
summary
Resilience is framed as adapting to the unexpected and building adaptive capacity — not perfect runbooks but the network, rituals and experiments that let teams change on the fly. Examples include treating runbooks as starting points, sending newcomers to run incidents to surface fresh insights, digging into dark code paths, pairing with customer support, and using AB tests to validate ideas. Layoffs erase institutional knowledge, shorthand chat reactions break coordination, and graceful extensibility describes how human expertise temporarily stretches systems. AI cannot substitute for this sociotechnical work.
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chapters (17)
- 0:00Introduction
- 1:07Resilience engineering today
- 3:14Adaptive capacity vs changes
- 5:01Naming hidden signals
- 8:39Preventing outages daily
- 10:04Improving system performance
- 13:12Sharing expertise
- 16:48Empowering early engineers
- 17:50Risk negotiation in teams
- 19:40Graceful extensibility
- 21:46Recognizing fatigue limits
- 23:13Risk and confidence balance
- 25:01Efficiency vs thoroughness
- 26:47Desire paths in systems
- 28:56Shortcuts under pressure
- 31:24AI cannot generate resilience
- 33:30Closing thoughts
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