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The Future of Network Troubleshooting

Jason Baudreau

We live in an age of technology dependency and increasing complexity. As companies adopt new hardware and applications, their networks grow larger and become harder to manage. For network engineers and administrators, the continued emergence of integrated technology has forced them to reconfigure and manage networks in a more dynamic way.

When a network stops working properly or stops working altogether, network engineers are called upon to troubleshoot and fix the problem. According to the Network Instruments State of the Network Global Survey, the majority of network professionals spend more than two months per year troubleshooting network performance problems. This time spent identifying and troubleshooting problems costs companies millions of dollars every year, and perhaps of equal importance, takes valuable time away from other critical projects and updates network engineers would prefer to work on.

The Current Network Situation

The current state of the industry calls for network engineers to use command-line interface (CLI) to provide visibility into a network for troubleshooting. Greater visibility into the network not only helps engineers identify a problem, but prevents it from happening in another sector of the network as well.

With CLI, engineers issue commands to a specific device or program through command lines, or lines of text. However, a major drawback of CLI is that engineers need to work through individual devices on a network one at a time to identify and fix a potential problem, which is a time intensive and resource draining practice.

To counteract some of the drawbacks of CLI, network engineers use other tools to assist them in troubleshooting. Regardless of the tools' benefits, such as availability and specification, none of these programs provide the information, network details or insight network engineers need to effectively prevent problems in the future or work through a major outage. While the industry has always used these in-line commands with additional tools, it's time to flip the practice of troubleshooting on its head.

The Future is Now

Through leveraging a dynamic network map as the single pane of glass for troubleshooting, engineers can automate network diagnoses and collaborate more effectively. These real-time maps provide engineers with visibility into the network's underlying design and performance, without the need for CLI digging. With these maps, engineers can quickly map the problem area and launch an executable runbook to automate network analytics, if and when a problem exists on the network.

These executable runbooks provide both a troubleshooting methodology and the automation to accelerate it. This single pane of glass view into the network also provides engineers with a snapshot of the entire network, allowing them to quickly answer the question "what changed?" if an outage occurs.

By integrating a network monitoring system with an adaptive automation platform, engineers have a current and instant view of all hardware and applications on the network to quickly diagnose any problems that may arise. Juxtapose that with manually recreating the network on the back of the pizza box.

As we continue to live in a world of complex technology, it is only right we continue to equip ourselves with the proper tools and tactics to succeed. When downtime mounts, the bottom line plummets and that is simply unacceptable.

Jason Baudreau is Product Strategist, Network Innovations, NetBrain Technologies.

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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 ...

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The Future of Network Troubleshooting

Jason Baudreau

We live in an age of technology dependency and increasing complexity. As companies adopt new hardware and applications, their networks grow larger and become harder to manage. For network engineers and administrators, the continued emergence of integrated technology has forced them to reconfigure and manage networks in a more dynamic way.

When a network stops working properly or stops working altogether, network engineers are called upon to troubleshoot and fix the problem. According to the Network Instruments State of the Network Global Survey, the majority of network professionals spend more than two months per year troubleshooting network performance problems. This time spent identifying and troubleshooting problems costs companies millions of dollars every year, and perhaps of equal importance, takes valuable time away from other critical projects and updates network engineers would prefer to work on.

The Current Network Situation

The current state of the industry calls for network engineers to use command-line interface (CLI) to provide visibility into a network for troubleshooting. Greater visibility into the network not only helps engineers identify a problem, but prevents it from happening in another sector of the network as well.

With CLI, engineers issue commands to a specific device or program through command lines, or lines of text. However, a major drawback of CLI is that engineers need to work through individual devices on a network one at a time to identify and fix a potential problem, which is a time intensive and resource draining practice.

To counteract some of the drawbacks of CLI, network engineers use other tools to assist them in troubleshooting. Regardless of the tools' benefits, such as availability and specification, none of these programs provide the information, network details or insight network engineers need to effectively prevent problems in the future or work through a major outage. While the industry has always used these in-line commands with additional tools, it's time to flip the practice of troubleshooting on its head.

The Future is Now

Through leveraging a dynamic network map as the single pane of glass for troubleshooting, engineers can automate network diagnoses and collaborate more effectively. These real-time maps provide engineers with visibility into the network's underlying design and performance, without the need for CLI digging. With these maps, engineers can quickly map the problem area and launch an executable runbook to automate network analytics, if and when a problem exists on the network.

These executable runbooks provide both a troubleshooting methodology and the automation to accelerate it. This single pane of glass view into the network also provides engineers with a snapshot of the entire network, allowing them to quickly answer the question "what changed?" if an outage occurs.

By integrating a network monitoring system with an adaptive automation platform, engineers have a current and instant view of all hardware and applications on the network to quickly diagnose any problems that may arise. Juxtapose that with manually recreating the network on the back of the pizza box.

As we continue to live in a world of complex technology, it is only right we continue to equip ourselves with the proper tools and tactics to succeed. When downtime mounts, the bottom line plummets and that is simply unacceptable.

Jason Baudreau is Product Strategist, Network Innovations, NetBrain Technologies.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...