Skip to main content

Puppet Labs Partners with Cisco on Automation of Next-Generation Networks

Puppet Labs announces the integration of Puppet Enterprise and Cisco Open NX-OS, in collaboration with Cisco as part of the Cisco Solution Partner Program.

Puppet Labs is making available a native Puppet NX-OS agent and Cisco Puppet Module. Cisco Open NX-OS will be delivered in Q3CY15 for the Cisco Nexus 3000 and 9000, and the integrated Puppet Labs solutions will be usable by customers upon delivery of this new version of the operating system. Puppet Labs is a Cisco Solution Partner and this compatible solution will bring infrastructure as code practices to the management of Cisco Nexus 3000 and 9000 Series Switches.

As increased competition and accelerating business pace force IT organizations to move faster while maintaining service availability, SDN has been embraced by many to increase operational speed and efficiency of network management. Infrastructure as code implements SDN to enable DevOps practices such as cross-team change collaboration, automated infrastructure testing, and automated application deployments that span compute, storage, and network.

“Cisco’s Open NX-OS platform will enable network administrators to automate more of their data center,” said Nigel Kersten, CIO of Puppet Labs. “Our collaboration with Cisco will enable our joint customers to realize the benefits of SDN implemented through infrastructure as code, and extend DevOps practices to more teams within IT operations.”

Cisco NX-OS, the operating system for Cisco Nexus switches, will support desired state automation through the new native Puppet Enterprise agent. The integration with Puppet Enterprise utilizes the programming capabilities and open APIs included in Cisco NX-OS and enables IT administrators to automate management and configuration of their network infrastructure.

“The new Cisco Open NX-OS platform for Nexus 9000 and 3000 Series Switches delivers a programmable API and object model to automate network configuration and management while taking advantage of familiar Linux toolsets,” said Frank D’Agostino, CTO of Technical Marketing for Cisco’s Insieme Business Unit. “The tight integration of Puppet Enterprise and Cisco Open NX-OS will provide greater SDN-based programmable infrastructure capabilities that enable advanced, automated DevOps capabilities for customers.”

Joint customers will be able to automate Day One tasks such as the configuration of SNMP, authentication, and logging, and also Day Two and Day Three tasks, such as configuration of port channels, VLANS, and dynamic routes. Teams will be able to deploy and monitor changes that span compute and networking — all with Puppet Enterprise.

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.

Puppet Labs Partners with Cisco on Automation of Next-Generation Networks

Puppet Labs announces the integration of Puppet Enterprise and Cisco Open NX-OS, in collaboration with Cisco as part of the Cisco Solution Partner Program.

Puppet Labs is making available a native Puppet NX-OS agent and Cisco Puppet Module. Cisco Open NX-OS will be delivered in Q3CY15 for the Cisco Nexus 3000 and 9000, and the integrated Puppet Labs solutions will be usable by customers upon delivery of this new version of the operating system. Puppet Labs is a Cisco Solution Partner and this compatible solution will bring infrastructure as code practices to the management of Cisco Nexus 3000 and 9000 Series Switches.

As increased competition and accelerating business pace force IT organizations to move faster while maintaining service availability, SDN has been embraced by many to increase operational speed and efficiency of network management. Infrastructure as code implements SDN to enable DevOps practices such as cross-team change collaboration, automated infrastructure testing, and automated application deployments that span compute, storage, and network.

“Cisco’s Open NX-OS platform will enable network administrators to automate more of their data center,” said Nigel Kersten, CIO of Puppet Labs. “Our collaboration with Cisco will enable our joint customers to realize the benefits of SDN implemented through infrastructure as code, and extend DevOps practices to more teams within IT operations.”

Cisco NX-OS, the operating system for Cisco Nexus switches, will support desired state automation through the new native Puppet Enterprise agent. The integration with Puppet Enterprise utilizes the programming capabilities and open APIs included in Cisco NX-OS and enables IT administrators to automate management and configuration of their network infrastructure.

“The new Cisco Open NX-OS platform for Nexus 9000 and 3000 Series Switches delivers a programmable API and object model to automate network configuration and management while taking advantage of familiar Linux toolsets,” said Frank D’Agostino, CTO of Technical Marketing for Cisco’s Insieme Business Unit. “The tight integration of Puppet Enterprise and Cisco Open NX-OS will provide greater SDN-based programmable infrastructure capabilities that enable advanced, automated DevOps capabilities for customers.”

Joint customers will be able to automate Day One tasks such as the configuration of SNMP, authentication, and logging, and also Day Two and Day Three tasks, such as configuration of port channels, VLANS, and dynamic routes. Teams will be able to deploy and monitor changes that span compute and networking — all with Puppet Enterprise.

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.