Opening
Software engineering evolved from manual releases to CI/CD pipelines, observability, and DevOps. Embodied AI inherited modern ML infrastructure, but not operational assurance infrastructure.
The missing layer is Safety DevOps.
Core Thesis
Modern robotics and autonomy require:
- continuously updated assurance
- automated evidence freshness tracking
- change-aware traceability
- deployment-aware verification
- operational approval workflows
Not just periodic certification exercises.
What Safety DevOps Means
Safety DevOps is:
- CI/CD for assurance
- continuously updated traceability
- automatic impact propagation
- evidence invalidation detection
- safety-aware deployment gating
- operational verification pipelines
Why Current Workflows Fail
Traditional workflows assume:
- system freezes
- safety analysis occurs
- testing completes
- safety case assembled
- product ships
Modern autonomy systems never stop evolving.
The Infrastructure Gap
Embodied AI already has:
- MLOps
- cloud infrastructure
- simulation infrastructure
- telemetry pipelines
- fleet management
But lacks:
- operational assurance infrastructure
- continuously synchronized safety state
- deployment-aware verification systems
What Changes
Every engineering change becomes:
- a propagation event
- an evidence freshness event
- a verification state event
- an assurance review event
Closing
Safety DevOps is not about automating sign-off. It is about making continuously evolving systems continuously understandable.