Operations | Monitoring | ITSM | DevOps | Cloud

That's Not a Job for an LLM: The Right Way to Apply AI to Network Operations

LLMs have sucked all the oxygen out of the AI conversation — but AI is much more than just LLMs, and network engineers have been using AI techniques (machine learning, statistics, fuzzy logic, expert systems, neural networks) for decades. So what should LLMs be doing in network operations, what shouldn't they be doing, and how do agentic AI architectures fit in?

Setting the Bar for Agentic NetOps

AI has quickly become part of the language of network observability. Many vendors across the observability landscape can describe, summarize, correlate, or explain some data or situation, leveraging basic LLM capabilities. At a distance, many of these offerings sound similar. They promise faster insight, efficient operations, and a more intelligent path through rising complexity. But the industry has reached a point where surface-level similarity is creating noise, not value.

Ephemeral Leaks and Automated BGP Route Leak Detection

Many BGP route leaks reported by automated detection systems are actually brief, low-impact artifacts of normal BGP convergence. Doug Madory examines examples from Cloudflare Radar, Routeviews, and Jared Mauch’s long-running leak detector to show how these “ephemeral leaks” arise, why they usually don’t disrupt traffic, and why they still matter for routing security.