The software factory stack everyone forgot to finish

Software factory models typically emphasize pre-merge activities such as planning, coding, testing, review, and security while treating post-merge production operations as limited to shipping and monitoring, a gap that becomes more significant as AI accelerates code generation. Survey and research findings cited from New Relic, DORA, and the Control Gap report suggest that AI may improve development throughput and perceived code quality but is also associated with delivery instability, production incidents, and greater caution around releases. The proposed “run side” of software delivery requires systems to identify what is actually running, retain immutable release and exposure records for auditing, apply automated policies for progressive exposure, reverse individual changes rather than entire deployments, and clean up obsolete flags and code branches. The author argues that these capabilities must function as automated safeguards rather than relying on human judgment, particularly when agents can make many rapid decisions. LaunchDarkly is presented as one implementation using feature flags, runtime metadata, change histories, release policies, guarded rollouts, and automated flag cleanup, while acknowledging that other implementations are possible and that consistent coverage across large, mixed technology estates remains difficult.

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