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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.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.