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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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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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...