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Netbrain Releases Next-Gen Release 12

NetBrain Technologies announced the launch of Next-Gen Release 12 (R12), a major update to its flagship platform.

New capabilities include a GenAI LLM Co-Pilot to assess, orchestrate and summarize network automation results with natural language, and Golden Engineering Studio (GES), which can reverse engineer a network’s design rules and features. This new release helps improve operational efficiency, minimize risk and maximize network performance – all while achieving critical security and compliance objectives.

R12’s new GES allows network and operations teams to analyze millions of lines of network configuration code to decode the live network’s configuration and state in minutes. Teams can discover these “Golden Configurations” and remediate any deviations on Day-1. GES can then be utilized to create no-code automation, as “Golden Intents,” to proactively verify the live network at scale – proactively preventing configuration drift, network outages and other security risks.

“Our early-adopting customers are in awe of the new reverse engineering capability within Golden Engineering Studio. Organizations often struggle to understand the intentions of the network’s original architects, which limits their ability to diagnose and address problems or anticipate the consequences of network changes,” said Song Pang, SVP of Engineering at NetBrain. “This helps them understand these intentions, detect problems and spot configuration drift before they cause outages.”

Another major feature of R12 is the new AI-powered Co-Pilot, which allows users to ask questions in natural language for intuitive problem resolution improving troubleshooting, change management and observability workflows. The Co-Pilot can rapidly orchestrate NetBrain automations, use intent-based reasoning to chain different actions together, and return the diagnosis results in natural language or summarize them into other formats such as a table, map or dashboard.

This AI Co-Pilot functions as a technology translator for human users to interact with no-code automation without training. It acts as a virtual self-service option that allows other operations and security teams to gather network information, saving critical NetOps resources for higher-level activities.

“Humans no longer have to figure out how to fix network problems alone. The fixes are already built into reverse-engineered golden configuration templates,” said Pang. “The ability to troubleshoot network problems, protect it for safe changes, and have real-time observability for proactive network operations is a game-changer for our clients.”

Other updates included in R12:

• Triple Defense Change Management: NetBrain R12 offers improved pre- and post-change validation that enables users to assess impacts to the network before, during and after network changes with GenAI. This prevents unintended consequences that negatively affect business-critical applications and services or introduce security vulnerabilities now and when making future changes. According to an EMA analyst report, 45% of network outages have root cause in configuration and change management errors.

• Hierarchical dashboard with Geo Location: Layers tailored, map-based device visibility onto summary network dashboards across organizational levels — company-wide, regional and senior management. Clear red/green indicators for network health and direct auto-remediation capabilities help streamline decision-making and enhance operational efficiency.

• Enhanced Ability for 3rd-Party Tools to Trigger Automations: NetBrain R12 upgrades integrations with critical 3rd-party tools like ServiceNow to execute network automations via API calls and retrieve execution results.

• Intent Programmability: Expanded data type support and improved UI for ease of use.

• Visual Parser Enhancements: New features for parsing and merging tables, and support for no-code API definitions.

• Automation Data Table Improvements: New views give multiple options for diverse data browsing and enhanced table-building.

NetBrain R12 is available on November 15 to all customers with active subscriptions or maintenance contracts at no additional charge.

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Netbrain Releases Next-Gen Release 12

NetBrain Technologies announced the launch of Next-Gen Release 12 (R12), a major update to its flagship platform.

New capabilities include a GenAI LLM Co-Pilot to assess, orchestrate and summarize network automation results with natural language, and Golden Engineering Studio (GES), which can reverse engineer a network’s design rules and features. This new release helps improve operational efficiency, minimize risk and maximize network performance – all while achieving critical security and compliance objectives.

R12’s new GES allows network and operations teams to analyze millions of lines of network configuration code to decode the live network’s configuration and state in minutes. Teams can discover these “Golden Configurations” and remediate any deviations on Day-1. GES can then be utilized to create no-code automation, as “Golden Intents,” to proactively verify the live network at scale – proactively preventing configuration drift, network outages and other security risks.

“Our early-adopting customers are in awe of the new reverse engineering capability within Golden Engineering Studio. Organizations often struggle to understand the intentions of the network’s original architects, which limits their ability to diagnose and address problems or anticipate the consequences of network changes,” said Song Pang, SVP of Engineering at NetBrain. “This helps them understand these intentions, detect problems and spot configuration drift before they cause outages.”

Another major feature of R12 is the new AI-powered Co-Pilot, which allows users to ask questions in natural language for intuitive problem resolution improving troubleshooting, change management and observability workflows. The Co-Pilot can rapidly orchestrate NetBrain automations, use intent-based reasoning to chain different actions together, and return the diagnosis results in natural language or summarize them into other formats such as a table, map or dashboard.

This AI Co-Pilot functions as a technology translator for human users to interact with no-code automation without training. It acts as a virtual self-service option that allows other operations and security teams to gather network information, saving critical NetOps resources for higher-level activities.

“Humans no longer have to figure out how to fix network problems alone. The fixes are already built into reverse-engineered golden configuration templates,” said Pang. “The ability to troubleshoot network problems, protect it for safe changes, and have real-time observability for proactive network operations is a game-changer for our clients.”

Other updates included in R12:

• Triple Defense Change Management: NetBrain R12 offers improved pre- and post-change validation that enables users to assess impacts to the network before, during and after network changes with GenAI. This prevents unintended consequences that negatively affect business-critical applications and services or introduce security vulnerabilities now and when making future changes. According to an EMA analyst report, 45% of network outages have root cause in configuration and change management errors.

• Hierarchical dashboard with Geo Location: Layers tailored, map-based device visibility onto summary network dashboards across organizational levels — company-wide, regional and senior management. Clear red/green indicators for network health and direct auto-remediation capabilities help streamline decision-making and enhance operational efficiency.

• Enhanced Ability for 3rd-Party Tools to Trigger Automations: NetBrain R12 upgrades integrations with critical 3rd-party tools like ServiceNow to execute network automations via API calls and retrieve execution results.

• Intent Programmability: Expanded data type support and improved UI for ease of use.

• Visual Parser Enhancements: New features for parsing and merging tables, and support for no-code API definitions.

• Automation Data Table Improvements: New views give multiple options for diverse data browsing and enhanced table-building.

NetBrain R12 is available on November 15 to all customers with active subscriptions or maintenance contracts at no additional charge.

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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 ...

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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.