Stories from the Factory Floor: Why AI software factories won’t always look like factories

LaunchDarkly argues that an AI software factory is not simply a collection of coding agents, but a set of technical and organizational systems that enable agents to work reliably across the software development lifecycle. For new services, the company recommends treating factory readiness as a production-readiness requirement, building automation, guardrails, and agent-friendly context alongside monitoring and operational practices from the start. Legacy codebases require a different approach: rather than assigning isolated tasks to agents, organizations should incrementally build supporting systems that reduce navigation friction, expose relevant knowledge, connect tools, capture decision-making context, and measure automation cost and effectiveness. The post describes two necessary layers of systems: the product and infrastructure agents operate within, and the automation infrastructure that supplies context, interoperability, observability, and sociotechnical signals. It encourages teams to view agent errors as system-design and ownership problems, investigate root causes, and create improvements that increase trust and autonomy instead of blaming models. LaunchDarkly also emphasizes collaboration, experimentation, feature-flagged iteration, and a culture that welcomes automation ideas, arguing that AI amplifies existing organizational strengths and weaknesses and that durable gains depend on improving the underlying engineering system.

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