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Kentik AI Released

Kentik launched Kentik AI to give any engineer, operator, architect, or developer the ability to troubleshoot complex networks quickly and easily.

The company is simultaneously launching a modern and AI-assisted Network Monitoring System (Kentik NMS) to enable teams to observe, manage, and optimize network health and performance via real-time monitoring and alerting.

"Historically, resolving complex network issues required network engineers to have years, if not decades, of experience," said Avi Freedman, CEO and Co-Founder of Kentik. "Now anyone — a developer, SRE, or business analyst — can ask questions about their network in their preferred language and get the answers they need."

Kentik has embedded Generative AI across its platform to democratize access to critical knowledge about complex systems. Through a natural language interface and guided troubleshooting workflows, Kentik now empowers teams to determine the root cause of customer-impacting issues much faster.

“We’re imagining a world where everybody can be a superstar network engineer. Where network insights are no longer limited to network experts. Where AI augments the engineer, dramatically increasing their productivity, speed of troubleshooting and remediation of issues, and as a result driving efficiencies that can’t reasonably be achieved by scaling manual resources,” said Christoph Pfister, Chief Product Officer at Kentik.

Kentik AI and platform innovations:

- Kentik Query Assistant: Kentik Query Assistant leverages a Large Language Model (LLM) infused with network context that enables users to ask questions about their network in natural language, and Kentik will use its full breadth of data to deliver an answer. This democratizes access to critical network insights that were typically available only to teams with deep network expertise.

- Kentik Journeys: Kentik Journeys provides users with an AI-assisted troubleshooting workflow to solve complex network problems. When troubleshooting, engineers ask a question, analyze the answer, then ask a new, better-informed question, drilling deeper into the issue. Kentik Journeys accelerates this process and expedites investigations with deep understanding of the user’s network.

- Kentik NMS: Kentik NMS is the first AI-assisted network monitoring system. It modernizes network monitoring technology by unifying traffic flow data with real-time, custom, and streaming device metrics in one extensible SaaS platform, allowing engineers to easily correlate heterogenous telemetry from distributed infrastructure and rapidly problem-solve.

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Kentik AI Released

Kentik launched Kentik AI to give any engineer, operator, architect, or developer the ability to troubleshoot complex networks quickly and easily.

The company is simultaneously launching a modern and AI-assisted Network Monitoring System (Kentik NMS) to enable teams to observe, manage, and optimize network health and performance via real-time monitoring and alerting.

"Historically, resolving complex network issues required network engineers to have years, if not decades, of experience," said Avi Freedman, CEO and Co-Founder of Kentik. "Now anyone — a developer, SRE, or business analyst — can ask questions about their network in their preferred language and get the answers they need."

Kentik has embedded Generative AI across its platform to democratize access to critical knowledge about complex systems. Through a natural language interface and guided troubleshooting workflows, Kentik now empowers teams to determine the root cause of customer-impacting issues much faster.

“We’re imagining a world where everybody can be a superstar network engineer. Where network insights are no longer limited to network experts. Where AI augments the engineer, dramatically increasing their productivity, speed of troubleshooting and remediation of issues, and as a result driving efficiencies that can’t reasonably be achieved by scaling manual resources,” said Christoph Pfister, Chief Product Officer at Kentik.

Kentik AI and platform innovations:

- Kentik Query Assistant: Kentik Query Assistant leverages a Large Language Model (LLM) infused with network context that enables users to ask questions about their network in natural language, and Kentik will use its full breadth of data to deliver an answer. This democratizes access to critical network insights that were typically available only to teams with deep network expertise.

- Kentik Journeys: Kentik Journeys provides users with an AI-assisted troubleshooting workflow to solve complex network problems. When troubleshooting, engineers ask a question, analyze the answer, then ask a new, better-informed question, drilling deeper into the issue. Kentik Journeys accelerates this process and expedites investigations with deep understanding of the user’s network.

- Kentik NMS: Kentik NMS is the first AI-assisted network monitoring system. It modernizes network monitoring technology by unifying traffic flow data with real-time, custom, and streaming device metrics in one extensible SaaS platform, allowing engineers to easily correlate heterogenous telemetry from distributed infrastructure and rapidly problem-solve.

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Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...