Building resilience through decision feedback loops
Engineering teams often invest in incident response but neglect structured reflection on their own choices. Resilient systems form when decisions are treated as testable hypotheses, and outcomes are reviewed systematically.
What is a decision feedback loop
A decision feedback loop is a process that:
- frames decisions as hypotheses
- monitors outcomes against intent
- updates practices based on what was learned
Shorter loops reduce the cost of mistakes and improve system adaptability.
Anatomy of a strong feedback loop
- Hypothesize: define assumptions and expected outcomes
- Act: implement based on current intent
- Sense: collect early indicators and unexpected signals
- Reflect: compare results with expectations, identify deltas
- Adjust: refine decision patterns, update shared heuristics
Why feedback loops get blocked
- Blame culture suppresses failure visibility
- Decisions are framed as final rather than testable
- Lack of telemetry hides weak or failing assumptions
Embedding feedback loops
- Define assumptions and success metrics up front
- Track whether assumptions held, not just delivery outcomes
- Run decision reviews to evolve heuristics, not to assign fault
Feedback loop health metrics
- Decision-to-insight latency: time from action to learning
- Assumption falsification rate: how often core assumptions break
- Heuristic evolution rate: percentage of practices updated over time