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NetBrain Introduces Agentic AI

NetBrain Technologies announced the next version of its NetBrain platform introducing Agentic NetOps. 

The release integrates agentic AI into core workflows to help NetOps teams by autonomously investigating and diagnosing complex network issues, suggesting fixes and supporting engineers in executing remediations. These new capabilities reduce resolution time, automate more manual NetOps workflows, and make network changes safer across increasingly complex hybrid-cloud environments.

“With this release, we’ve delivered agentic AI as a full-fledged digital engineer – it can diagnose complex issues independently dramatically improving the speed that teams can resolve incidents driving to the ultimate goal of preventing network downtime,” said Song Pang, Chief Technology Officer at NetBrain. “We understand our customers are at various stages of AI adoption. Our goal is to meet them where they are and make available in-the-loop, on-the-loop, and out-of-the-loop in support of our goal of reducing outages for our customers by 50% this year, and every year thereafter.”

NetBrain has steadily expanded the AI capabilities in recent versions of its platform. Building on its intent-based automation and digital twin technology, Agentic AI makes diagnosis and remediation of network problems faster, more accurate, and more automated, while keeping engineers in control of execution. Furthermore, NetBrain has dramatically increased its support of services for all major cloud providers as well as full Kubernetes integration, providing better visibility across complex hybrid and containerized networks.

Specific new and expanded features include:

  • AI Deep Diagnosis – Human-on-the-loop Agentic AI that leverages NetBrain’s digital twin and intent-based automations to analyze network issues, providing transparent, step-by-step reasoning and visualized root-cause results on a map for fast incident resolution. It helps junior staff work like experts and engineers solve faster by turning complex automation into guided steps to save time and reduce guesswork.
  • AI Runbook Companion – AI helps plan and recommend actions by building runbooks, keeping engineering in control with human-in-the-loop to approve and execute automation. AI reasons through the data and recommends next steps accelerate the troubleshooting workflow.
  • Extended cloud support for over 200 services expands intent-based automation coverage beyond traditional networking to resource management, data, and compute services meets high demand. View physical and logical nodes including database, caching, DNS, storage, and other managed services in a single dashboard to help accelerate identification and troubleshooting of problems across the hybrid-network.
  • Quick Assessment - automates network validation and troubleshooting checks across multiple devices, transforming hours of manual checks into minutes and streamlining operationally heavy workflows to maintain network security and compliance.
  • Automated change remediation with optional pre-approval workflows enables safer, faster network updates, enforcing compliance, reducing manual steps, and centralizing change management and oversight.

NetBrain delivers Agentic NetOps, the evolution of network automation where AI becomes an autonomous digital engineer. This enables proactive intelligence, faster resolution, and safer changes across hybrid-cloud environments. The platform helps NetOps teams at the largest organizations in the world manage complex, hybrid networks with AI and automation.

“We believe customers should expect to improve the availability of their networks every year, reducing outages 50%, year over year – even as networks become more complex,” shared Barbara Scarcella, Chief Customer Officer for NetBrain, “our approach to Agentic NetOps along with our years of best practices delivers this continuous improvement. Our platform moves toward increasingly autonomous remediation as it gains data and runbooks tailored to each customer’s unique network.

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.

NetBrain Introduces Agentic AI

NetBrain Technologies announced the next version of its NetBrain platform introducing Agentic NetOps. 

The release integrates agentic AI into core workflows to help NetOps teams by autonomously investigating and diagnosing complex network issues, suggesting fixes and supporting engineers in executing remediations. These new capabilities reduce resolution time, automate more manual NetOps workflows, and make network changes safer across increasingly complex hybrid-cloud environments.

“With this release, we’ve delivered agentic AI as a full-fledged digital engineer – it can diagnose complex issues independently dramatically improving the speed that teams can resolve incidents driving to the ultimate goal of preventing network downtime,” said Song Pang, Chief Technology Officer at NetBrain. “We understand our customers are at various stages of AI adoption. Our goal is to meet them where they are and make available in-the-loop, on-the-loop, and out-of-the-loop in support of our goal of reducing outages for our customers by 50% this year, and every year thereafter.”

NetBrain has steadily expanded the AI capabilities in recent versions of its platform. Building on its intent-based automation and digital twin technology, Agentic AI makes diagnosis and remediation of network problems faster, more accurate, and more automated, while keeping engineers in control of execution. Furthermore, NetBrain has dramatically increased its support of services for all major cloud providers as well as full Kubernetes integration, providing better visibility across complex hybrid and containerized networks.

Specific new and expanded features include:

  • AI Deep Diagnosis – Human-on-the-loop Agentic AI that leverages NetBrain’s digital twin and intent-based automations to analyze network issues, providing transparent, step-by-step reasoning and visualized root-cause results on a map for fast incident resolution. It helps junior staff work like experts and engineers solve faster by turning complex automation into guided steps to save time and reduce guesswork.
  • AI Runbook Companion – AI helps plan and recommend actions by building runbooks, keeping engineering in control with human-in-the-loop to approve and execute automation. AI reasons through the data and recommends next steps accelerate the troubleshooting workflow.
  • Extended cloud support for over 200 services expands intent-based automation coverage beyond traditional networking to resource management, data, and compute services meets high demand. View physical and logical nodes including database, caching, DNS, storage, and other managed services in a single dashboard to help accelerate identification and troubleshooting of problems across the hybrid-network.
  • Quick Assessment - automates network validation and troubleshooting checks across multiple devices, transforming hours of manual checks into minutes and streamlining operationally heavy workflows to maintain network security and compliance.
  • Automated change remediation with optional pre-approval workflows enables safer, faster network updates, enforcing compliance, reducing manual steps, and centralizing change management and oversight.

NetBrain delivers Agentic NetOps, the evolution of network automation where AI becomes an autonomous digital engineer. This enables proactive intelligence, faster resolution, and safer changes across hybrid-cloud environments. The platform helps NetOps teams at the largest organizations in the world manage complex, hybrid networks with AI and automation.

“We believe customers should expect to improve the availability of their networks every year, reducing outages 50%, year over year – even as networks become more complex,” shared Barbara Scarcella, Chief Customer Officer for NetBrain, “our approach to Agentic NetOps along with our years of best practices delivers this continuous improvement. Our platform moves toward increasingly autonomous remediation as it gains data and runbooks tailored to each customer’s unique network.

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.