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Kentik Releases Cause Analysis

Kentik launched Cause Analysis, a new AI-powered capability that automatically identifies and explains the cause of network traffic issues and anomalies which result in slow speeds, dropped connections, packet loss, and other service disruptions caused by factors like network congestion and hardware failures. 

Starting today, when sudden performance degradation, cost spikes, and traffic changes arise, any SRE or platform engineer can investigate traffic changes without needing a deep understanding of the network.

“Over the past few years, we’ve seen the rise of the SRE and platform engineer within modern enterprises, but network experts are far and few between,” said Avi Freedman, CEO and co-founder of Kentik. “When you need to diagnose a network issue impacting customers, Cause Analysis can pinpoint changes impacting customers in seconds and explain in plain language what it could take even a network expert many query iterations to diagnose and understand.”

Designed with customers to help engineers more quickly understand the underlying network traffic contributing to real network anomalies in real workflows, Cause Analysis goes beyond alert noise reduction by leveraging artificial intelligence to pinpoint the causes of performance degradation and network outages. Behind the scenes, proprietary algorithms are running to quickly identify traffic sources and diagnose issues within complex modern networks. Those findings are then filtered through customers’ chosen LLM to render a natural language explanation of the underlying issues.

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Kentik Releases Cause Analysis

Kentik launched Cause Analysis, a new AI-powered capability that automatically identifies and explains the cause of network traffic issues and anomalies which result in slow speeds, dropped connections, packet loss, and other service disruptions caused by factors like network congestion and hardware failures. 

Starting today, when sudden performance degradation, cost spikes, and traffic changes arise, any SRE or platform engineer can investigate traffic changes without needing a deep understanding of the network.

“Over the past few years, we’ve seen the rise of the SRE and platform engineer within modern enterprises, but network experts are far and few between,” said Avi Freedman, CEO and co-founder of Kentik. “When you need to diagnose a network issue impacting customers, Cause Analysis can pinpoint changes impacting customers in seconds and explain in plain language what it could take even a network expert many query iterations to diagnose and understand.”

Designed with customers to help engineers more quickly understand the underlying network traffic contributing to real network anomalies in real workflows, Cause Analysis goes beyond alert noise reduction by leveraging artificial intelligence to pinpoint the causes of performance degradation and network outages. Behind the scenes, proprietary algorithms are running to quickly identify traffic sources and diagnose issues within complex modern networks. Those findings are then filtered through customers’ chosen LLM to render a natural language explanation of the underlying issues.

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For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

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My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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