Static network baselines won't survive agentic AI
Enterprise networks are entering a new phase in how AI is applied, moving beyond analytics dashboards and retrospective insights toward systems that recommend actions, optimize behavior, and operate closer to real time.
As AI becomes more agentic, one requirement becomes clear: systems that influence the network must continuously refine their understanding of what “normal” looks like. This is the principle behind recursive learning:
What is recursive learning?
Recursive learning is closest to what machine learning literature calls continual or online learning.
The distinction is that AI systems in dynamic environments should treat their reference model as something that evolves with the environment rather than something set once and periodically refreshed, with each calibration cycle informing the next.
Yet most enterprise deployments still rely on periodic baseline updates. A recursive system instead treats its current understanding of “normal” as provisional, evaluating changes against performance, experience, and risk.
Healthy outcomes...
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