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Kentik AI Advisor Released

Kentik launched Kentik AI Advisor – an artificial intelligence that deeply understands enterprise and service provider networks, thinks critically, and provides guidance for designing, operating, and protecting infrastructure at scale.

Kentik AI Advisor drives massive efficiencies across network, cloud, and infrastructure teams, and revolutionizes how companies approach network management and performance. The AI can interpret intent, build a plan based on rich telemetry from the Kentik platform, and then execute that plan reliably and securely.

Kentik AI Advisor leverages the proprietary Kentik Data Engine – an ultra-scalable, real-time data platform that ingests a trillion telemetry points per day – in order to unify cloud, device, flow, and internet data. AI Advisor combines advanced LLM and reasoning model capabilities with Kentik’s deep network expertise and engineering context to interpret intent, plan investigations, and explain its logic every step of the way.

Benefits of using the Kentik AI Advisor include:

  • Cost Optimization: Kentik AI Advisor automates work needed to identify cost efficiencies across your entire network. Whether on-prem, in the cloud, or across both – Kentik AI Advisor can automatically sift through all your network data to uncover opportunities to reduce VPC and transit costs, optimize peering and interconnects, and evaluate high-cost routes to improve your overall cost structure.
  • Capacity Planning: Kentik AI Advisor automatically analyzes utilization trends, forecasts run-out scenarios, and recommends optimal infrastructure investments. This innovation transforms capacity planning from reactive guesswork into proactive, data-driven intelligence.
  • Rapid Troubleshooting and DDoS Investigation: Kentik AI Advisor accelerates incident response and cuts MTTR by correlating flow, device, and cloud data to quickly pinpoint root causes and separate real threats from background noise. It then delivers clear, expert recommendations to guide fast mitigation and restore service stability.
  • Integrated Institutional Knowledge: Kentik AI Advisor goes beyond foundational LLM knowledge, leveraging internal runbooks and custom network context to deliver tailored value and insights based on unique business needs.

“Kentik AI Advisor is designed for modern infrastructure teams that are tasked with scaling operations, optimizing costs, and safeguarding digital assets — all while facing talent shortages,” said Avi Freedman, CEO and Co-Founder of Kentik. “Unlike traditional AIOps which correlates and reduces only across the alerts generated for it, AI Advisor leverages its deep contextual understanding of every network to research and identify issues, analyze trends, and recommend precise actions.”

The Latest

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Kentik AI Advisor Released

Kentik launched Kentik AI Advisor – an artificial intelligence that deeply understands enterprise and service provider networks, thinks critically, and provides guidance for designing, operating, and protecting infrastructure at scale.

Kentik AI Advisor drives massive efficiencies across network, cloud, and infrastructure teams, and revolutionizes how companies approach network management and performance. The AI can interpret intent, build a plan based on rich telemetry from the Kentik platform, and then execute that plan reliably and securely.

Kentik AI Advisor leverages the proprietary Kentik Data Engine – an ultra-scalable, real-time data platform that ingests a trillion telemetry points per day – in order to unify cloud, device, flow, and internet data. AI Advisor combines advanced LLM and reasoning model capabilities with Kentik’s deep network expertise and engineering context to interpret intent, plan investigations, and explain its logic every step of the way.

Benefits of using the Kentik AI Advisor include:

  • Cost Optimization: Kentik AI Advisor automates work needed to identify cost efficiencies across your entire network. Whether on-prem, in the cloud, or across both – Kentik AI Advisor can automatically sift through all your network data to uncover opportunities to reduce VPC and transit costs, optimize peering and interconnects, and evaluate high-cost routes to improve your overall cost structure.
  • Capacity Planning: Kentik AI Advisor automatically analyzes utilization trends, forecasts run-out scenarios, and recommends optimal infrastructure investments. This innovation transforms capacity planning from reactive guesswork into proactive, data-driven intelligence.
  • Rapid Troubleshooting and DDoS Investigation: Kentik AI Advisor accelerates incident response and cuts MTTR by correlating flow, device, and cloud data to quickly pinpoint root causes and separate real threats from background noise. It then delivers clear, expert recommendations to guide fast mitigation and restore service stability.
  • Integrated Institutional Knowledge: Kentik AI Advisor goes beyond foundational LLM knowledge, leveraging internal runbooks and custom network context to deliver tailored value and insights based on unique business needs.

“Kentik AI Advisor is designed for modern infrastructure teams that are tasked with scaling operations, optimizing costs, and safeguarding digital assets — all while facing talent shortages,” said Avi Freedman, CEO and Co-Founder of Kentik. “Unlike traditional AIOps which correlates and reduces only across the alerts generated for it, AI Advisor leverages its deep contextual understanding of every network to research and identify issues, analyze trends, and recommend precise actions.”

The Latest

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...