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Troubleshooting's Secret Weapon: Greater Visibility

Jason Baudreau

Today's network engineers have their work cut out for them. Bigger, more complex networks have created an environment where network engineers are forced to adapt and develop more effective ways to manage and troubleshoot their networks. This begins with better visibility, which has presented an issue traditionally as engineers struggle to create an accurate picture due to challenges with static maps.

87 percent of survey respondents primarily rely on manual techniques to create and update their network diagrams

Network engineers typically spend hours or even days at a time manually mapping out network diagrams. In fact, NetBrain's State of the Network Engineer study found that 87 percent of survey respondents primarily rely on manual techniques to create and update their network diagrams. However, by the time these diagrams are complete, they are already out-of-date and therefore useless.

This is where automation comes in. By automating the documentation process, any part of the network can be visualized in seconds with infinite detail. In addition, these maps will update automatically each time there is a change to the network, so engineers can be sure they are always seeing a thorough and accurate picture.

Manual documentation is wrought with inefficiencies. When it comes to a network outage or breach, network engineers are working against the clock to get back online. Manual methods and static documents only add to the frustration and time to repair.

Currently, most network engineers rely on a combination of manual techniques for troubleshooting, including traceroute and the command-line interface (CLI) to gain visibility into a network. However, these are tedious and time-consuming methods that force engineers to work through one device at a time to identify and address an issue. In fact, 43 percent of survey respondents stated that troubleshooting takes too much time using CLI. This can result in an overreliance on tribal knowledge and a slew of other tools to access critical information such as configuration details and performance data. This will hinder a network teams' ability to troubleshoot issues quickly by having to cycle back and forth between applications.

So, what's the alternative to network engineers suffering though hours of manual documentation to find the route of an outage? A Dynamic Map.

Dynamic Maps integrate with network teams' existing ticketing systems, monitoring tools, security and event management systems to create an all-encompassing tool. When an issue arises, a Dynamic Map can be created instantly to target the problem by simply identifying the source and destination IP addresses. Through automation, it can then be used to diagnose the connectivity, performance and configuration of each interface. The ability to immediately identify the source of an issue significantly reduces a network team's mean time to repair (MTTR), which can positively impact a company's bottom line. The longer the network is down, the longer the organization loses out on revenue.

With the abundance of new trends like AI/machine learning, SD-WAN and DevOps, it's unclear what exactly the networking industry will look like in the future. One thing we do know for certain — networks will only become more complex and difficult to manage. Automation will become the secret ingredient to network management, arming engineers with much-needed visibility as day-to-day workflows become nearly impossible to complete manually. Enterprises not prepared for these changes and who are without an accurate picture of their network will ultimately suffer.

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Troubleshooting's Secret Weapon: Greater Visibility

Jason Baudreau

Today's network engineers have their work cut out for them. Bigger, more complex networks have created an environment where network engineers are forced to adapt and develop more effective ways to manage and troubleshoot their networks. This begins with better visibility, which has presented an issue traditionally as engineers struggle to create an accurate picture due to challenges with static maps.

87 percent of survey respondents primarily rely on manual techniques to create and update their network diagrams

Network engineers typically spend hours or even days at a time manually mapping out network diagrams. In fact, NetBrain's State of the Network Engineer study found that 87 percent of survey respondents primarily rely on manual techniques to create and update their network diagrams. However, by the time these diagrams are complete, they are already out-of-date and therefore useless.

This is where automation comes in. By automating the documentation process, any part of the network can be visualized in seconds with infinite detail. In addition, these maps will update automatically each time there is a change to the network, so engineers can be sure they are always seeing a thorough and accurate picture.

Manual documentation is wrought with inefficiencies. When it comes to a network outage or breach, network engineers are working against the clock to get back online. Manual methods and static documents only add to the frustration and time to repair.

Currently, most network engineers rely on a combination of manual techniques for troubleshooting, including traceroute and the command-line interface (CLI) to gain visibility into a network. However, these are tedious and time-consuming methods that force engineers to work through one device at a time to identify and address an issue. In fact, 43 percent of survey respondents stated that troubleshooting takes too much time using CLI. This can result in an overreliance on tribal knowledge and a slew of other tools to access critical information such as configuration details and performance data. This will hinder a network teams' ability to troubleshoot issues quickly by having to cycle back and forth between applications.

So, what's the alternative to network engineers suffering though hours of manual documentation to find the route of an outage? A Dynamic Map.

Dynamic Maps integrate with network teams' existing ticketing systems, monitoring tools, security and event management systems to create an all-encompassing tool. When an issue arises, a Dynamic Map can be created instantly to target the problem by simply identifying the source and destination IP addresses. Through automation, it can then be used to diagnose the connectivity, performance and configuration of each interface. The ability to immediately identify the source of an issue significantly reduces a network team's mean time to repair (MTTR), which can positively impact a company's bottom line. The longer the network is down, the longer the organization loses out on revenue.

With the abundance of new trends like AI/machine learning, SD-WAN and DevOps, it's unclear what exactly the networking industry will look like in the future. One thing we do know for certain — networks will only become more complex and difficult to manage. Automation will become the secret ingredient to network management, arming engineers with much-needed visibility as day-to-day workflows become nearly impossible to complete manually. Enterprises not prepared for these changes and who are without an accurate picture of their network will ultimately suffer.

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 ...