Operations | Monitoring | ITSM | DevOps | Cloud

Yesterday’s widespread Cloudflare outage reminds us how crucial external dependencies are to the stability of our own applications. When a key edge provider like Cloudflare goes down, the impact on your internal monitoring systems can look like a catastrophic, internal system failure triggering a massive storm of alerts and sending engineering teams into frantic, misdirected debugging sessions.

AI in network operations is evolving faster than ever. But while new models and agents are emerging almost daily, they’re often working alone, with each confined to its own context, data, and domain. One model might analyze telemetry, another handles automation scripts, and a third generates summaries or recommendations. Each model might be intelligent on its own, but without a way to share context, they end up thinking in isolation, limiting what they can achieve together.

Over the last couple of years, nearly every network and infrastructure observability platform has added the word “AI” to its messaging. Some have introduced helpful capabilities. Others have simply added a chatbot on top of the same dashboards that have existed for a decade. In many ways, the term has started to lose meaning. But inside network operations, the conversation hasn’t disappeared. It has simply become more blunt.