Static network baselines won't survive agentic AI (www.techradar.com)

Enterprise networks are shifting towards more advanced AI applications that recommend real-time actions and optimize behavior, moving away from static analytics. This transformation hinges on the concept of recursive learning, where AI systems continuously refine their understanding of "normal" behaviors based on real-time outcomes rather than relying on outdated baselines. This approach enables networks to adjust their expectations dynamically, resulting in fewer false positives and allowing operator attention to focus where it's truly needed. Recursive learning is especially pivotal for network operations due to the speed of feedback loops, allowing rapid adaptation to changing conditions. By integrating telemetry from multiple domains, recursive learning not only enhances accuracy but also manages configuration drift, which is a common cause of outages. This method reframes drift from a hygiene issue to a continuous operational condition that needs management. Ultimately, organizations must embrace recursive learning as an operational discipline to build a robust foundation for agentic AI systems, ensuring they can effectively handle the complexities of modern network environments.