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Kentik Integrates with ServiceNow

Kentik announced an agentic AI integration with ServiceNow. 

Kentik provides enterprises with complete visibility across their networks, combining a natural language interface with automated context enrichment to enable rapid optimization of on-prem, hybrid, and cloud networks.

With this integration, ServiceNow ITOM customers are able to address complex network issues without having to leave the ServiceNowⓇ AI Platform. Kentik’s Network Intelligence provides a natural language interface that empowers ServiceNow customers, even those with limited network experience, to self-serve network insights and rapidly answer critical questions about connectivity, performance, capacity, cost, and beyond.

“We’re providing a powerful understanding of the network layer for all,” said Avi Freedman, Founder and CEO of Kentik. “And for seasoned network engineers, we’re massively reducing toil, so that they can focus on mission-critical work that drives business outcomes.”

Kentik’s new agentic integration with AI Workflows enables ServiceNow ITOM users to quickly resolve network issues and improve customer experience. With code velocity increasing by 70% following the rise of AI coding assistants (according to the latest DORA report), IT and network teams are drowning in data as they waste time and energy switching between myriad tools in order to troubleshoot problems. Kentik delivers instant answers to any network question, uncovering cost savings, hardening security posture, and powering application performance.

“AI agents drive productivity gains across all lines of business,” said Gab Menachem, vice president, product, ITOM at ServiceNow. “By integrating Kentik Network Intelligence into ServiceNow’s AI agent workflows, businesses can break down data silos and empower the entire IT organization with network insights that deliver business value.”

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Kentik Integrates with ServiceNow

Kentik announced an agentic AI integration with ServiceNow. 

Kentik provides enterprises with complete visibility across their networks, combining a natural language interface with automated context enrichment to enable rapid optimization of on-prem, hybrid, and cloud networks.

With this integration, ServiceNow ITOM customers are able to address complex network issues without having to leave the ServiceNowⓇ AI Platform. Kentik’s Network Intelligence provides a natural language interface that empowers ServiceNow customers, even those with limited network experience, to self-serve network insights and rapidly answer critical questions about connectivity, performance, capacity, cost, and beyond.

“We’re providing a powerful understanding of the network layer for all,” said Avi Freedman, Founder and CEO of Kentik. “And for seasoned network engineers, we’re massively reducing toil, so that they can focus on mission-critical work that drives business outcomes.”

Kentik’s new agentic integration with AI Workflows enables ServiceNow ITOM users to quickly resolve network issues and improve customer experience. With code velocity increasing by 70% following the rise of AI coding assistants (according to the latest DORA report), IT and network teams are drowning in data as they waste time and energy switching between myriad tools in order to troubleshoot problems. Kentik delivers instant answers to any network question, uncovering cost savings, hardening security posture, and powering application performance.

“AI agents drive productivity gains across all lines of business,” said Gab Menachem, vice president, product, ITOM at ServiceNow. “By integrating Kentik Network Intelligence into ServiceNow’s AI agent workflows, businesses can break down data silos and empower the entire IT organization with network insights that deliver business value.”

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...