Operations | Monitoring | ITSM | DevOps | Cloud

For much of the past decade, many of the most ambitious ideas in artificial intelligence lived primarily in research papers, labs, and long-term roadmaps. Agentic AI was no exception. The concept of AI systems capable of reasoning, planning, and acting autonomously was widely discussed but largely theoretical. But earlier this month, Gartner released its report The Future of NetOps Is Agentic, reflecting a growing consensus that this has changed. What was once conceptual is now becoming operational.

Metrics, analytics, measurements, and parameters – can we truly see these abstractions? Data visualization helps us do just that, bridging the gap between raw information and human comprehension. Visualizing data is like rafting down a river – dynamic, unpredictable, and full of discoveries along the way. In this guide, we’ll explore how to craft visualizations that inform, engage, and inspire. So, grab your paddle and hop aboard!

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.

Across the IT and observability landscape, enterprise leaders are facing a troubling pattern. A trusted vendor announces a “modernization initiative,” often following a major acquisition or a shift in ownership. Overnight, pricing structures change, license models disappear, and long-time customers are pressured into multi-year bundles under the banner of innovation. What’s being framed as progress often feels more like pressure.

For years, the promise of AI-driven network automation has loomed large. Vendors and analysts alike have painted a future where autonomous operations handle outages before they happen, root causes are explained instantly, and teams finally escape the endless cycle of alerts, tickets, and manual troubleshooting. But in practice, most automation initiatives stall long before they reach that vision.

When MIT released research showing that 95% of enterprise AI pilots fail to deliver measurable business impact, it made headlines for a reason. After years of heavy investment in artificial intelligence, the vast majority of organizations still haven’t moved beyond pilots that promise much but deliver little. This doesn’t mean AI itself is broken. In most cases, the technology performs as intended.

The hype around generative AI has led many IT teams to experiment with plugging generic GPT models into their workflows. On paper, this is the beginning of true AI networking, featuring conversational interfaces, instant summaries, and faster troubleshooting. However, as we discussed in the previous post, “Why Your IT Copilot Needs Context, Not Just Data,” copilots are only as effective as the intelligence behind them.

In the rush to adopt AI in IT operations, many organizations focus on feeding copilots as much data as possible. But here’s the problem: data without context is just noise. An IT copilot that can’t distinguish what matters from what doesn’t won’t reduce alert fatigue or accelerate troubleshooting.