When Networks Learn to Manage Themselves: The Shift from Manual Control to Intelligent Autonomy RAN automation · ML · closed-loop control 10 min read Manual network management stopped scaling before most operators admitted it. The breaking point was not a single event; it was a gradual accumulation of complexity that outpaced the feedback loops humans could act within. By the time this became impossible to ignore, the tools to address it were already being built. The transition from reactive troubleshooting to intelligent autonomy was not a product decision. It emerged from a specific operational reality: networks were generating more state changes, more counter combinations, and more parameter interactions than any team could reason about simultaneously. The only sustainable response was to make the network observable first, then actionable, then self-correcting. Why manual control break at this scale The engineering...