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ScienceLogic Announces Skylar One Updates

ScienceLogic announced the first major update to its flagship Skylar™ One platform since its rebrand and expansion from SL1™.

The release delivers a faster, intuitive user interface, compelling visualization capabilities, and deeper network and automated system intelligence for large-scale enterprises and service providers.

The enhancements reflect ScienceLogic's continuous evolution toward realizing the Autonomic IT end state of intelligent, self-managing operations that anticipate and resolve issues automatically. With this release, Skylar One strengthens its foundations as part of the ScienceLogic AI Platform, uniting observability, automation, compliance and AI into one platform that enables IT leaders to move faster and with greater confidence and clarity in an increasingly agentic world.

“Skylar One Juneau is a significant release for us,” said Michael Nappi, Chief Product Officer at ScienceLogic. “We are at an inflection point where observability, AI and automation have converged, and this release makes that real for customers. By bundling advances in topology, geo aware visibility and high throughput data pipelines into a single AI driven platform, we give operations teams a much clearer sense of what is happening, where it is happening and how to respond with confidence.”

This latest release introduces measurable gains in platform performance and resilience, including:

  • Greater scalability and speed: Enhanced data ingestion and collection pipelines increase throughput by up to 60%, improving responsiveness for large-scale hybrid environments and providing the headroom needed to accommodate rapid growth without sacrificing stability or insights.
  • Context-rich situational awareness: New Geo Maps provide dynamic, location-based visualization of monitored assets, enabling teams to quickly identify regional or site-level issues. By correlating device health and service status with intelligent geography, operations teams can prioritize response, assess business impact, and streamline incident triage across distributed environments.
  • Improved reliability and uptime: Optimized high-availability failover and multi-proxy agent support strengthen connection stability and reduce service interruptions, so critical systems remain continuously available and resilient under load.
  • Smarter network visibility: A rebuilt topology engine with enhanced intelligence surrounding Layer2/Layer3, Link Layer Discovery Protocol (LLDP), and Cisco Discovery Protocol (CDP) processing provides accurate dependency mapping and better event correlation across hybrid environments so IT teams can pinpoint root causes faster, reduce downtime, and maintain consistent service performance.
  • Expanded AI workload observability: New AMD GPU monitoring adds deeper visibility into AI workloads by correlating GPU health and performance with infrastructure and service context. This gives teams clearer insight into resource usage, constraints and potential performance and cost risks, helping them take more proactive action across AI driven environments.
  • Simplified operations: A redesigned modern interface introduces faster navigation and improved service visualizations so teams can diagnose and resolve issues more quickly and improve overall operator efficiency.

The updates also reinforce ScienceLogic’s commitment to secure and compliant operations, supporting government and enterprise standards including Security Technical Implementation Guide (STIG) and FedRAMP environments to ensure observability and automation remain trusted across regulated sectors. These advancements strengthen the platform’s ability to support AI-driven observability and closed-loop automation, helping IT teams move from reactive troubleshooting to proactive, predictive operations aligned to business outcomes.

Skylar One is a foundation for autonomous, data-driven, and trusted IT operations. The platform’s unified intelligence allows teams to see everything, understand context instantly, and act with confidence through explainable AI and verifiable automation. Together with Skylar™ AI, Skylar™ Automation, and Skylar™ Compliance, the ScienceLogic AI Platform transforms IT from reactive firefighting to proactive, self-optimizing operations built on trust and data integrity. 

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ScienceLogic Announces Skylar One Updates

ScienceLogic announced the first major update to its flagship Skylar™ One platform since its rebrand and expansion from SL1™.

The release delivers a faster, intuitive user interface, compelling visualization capabilities, and deeper network and automated system intelligence for large-scale enterprises and service providers.

The enhancements reflect ScienceLogic's continuous evolution toward realizing the Autonomic IT end state of intelligent, self-managing operations that anticipate and resolve issues automatically. With this release, Skylar One strengthens its foundations as part of the ScienceLogic AI Platform, uniting observability, automation, compliance and AI into one platform that enables IT leaders to move faster and with greater confidence and clarity in an increasingly agentic world.

“Skylar One Juneau is a significant release for us,” said Michael Nappi, Chief Product Officer at ScienceLogic. “We are at an inflection point where observability, AI and automation have converged, and this release makes that real for customers. By bundling advances in topology, geo aware visibility and high throughput data pipelines into a single AI driven platform, we give operations teams a much clearer sense of what is happening, where it is happening and how to respond with confidence.”

This latest release introduces measurable gains in platform performance and resilience, including:

  • Greater scalability and speed: Enhanced data ingestion and collection pipelines increase throughput by up to 60%, improving responsiveness for large-scale hybrid environments and providing the headroom needed to accommodate rapid growth without sacrificing stability or insights.
  • Context-rich situational awareness: New Geo Maps provide dynamic, location-based visualization of monitored assets, enabling teams to quickly identify regional or site-level issues. By correlating device health and service status with intelligent geography, operations teams can prioritize response, assess business impact, and streamline incident triage across distributed environments.
  • Improved reliability and uptime: Optimized high-availability failover and multi-proxy agent support strengthen connection stability and reduce service interruptions, so critical systems remain continuously available and resilient under load.
  • Smarter network visibility: A rebuilt topology engine with enhanced intelligence surrounding Layer2/Layer3, Link Layer Discovery Protocol (LLDP), and Cisco Discovery Protocol (CDP) processing provides accurate dependency mapping and better event correlation across hybrid environments so IT teams can pinpoint root causes faster, reduce downtime, and maintain consistent service performance.
  • Expanded AI workload observability: New AMD GPU monitoring adds deeper visibility into AI workloads by correlating GPU health and performance with infrastructure and service context. This gives teams clearer insight into resource usage, constraints and potential performance and cost risks, helping them take more proactive action across AI driven environments.
  • Simplified operations: A redesigned modern interface introduces faster navigation and improved service visualizations so teams can diagnose and resolve issues more quickly and improve overall operator efficiency.

The updates also reinforce ScienceLogic’s commitment to secure and compliant operations, supporting government and enterprise standards including Security Technical Implementation Guide (STIG) and FedRAMP environments to ensure observability and automation remain trusted across regulated sectors. These advancements strengthen the platform’s ability to support AI-driven observability and closed-loop automation, helping IT teams move from reactive troubleshooting to proactive, predictive operations aligned to business outcomes.

Skylar One is a foundation for autonomous, data-driven, and trusted IT operations. The platform’s unified intelligence allows teams to see everything, understand context instantly, and act with confidence through explainable AI and verifiable automation. Together with Skylar™ AI, Skylar™ Automation, and Skylar™ Compliance, the ScienceLogic AI Platform transforms IT from reactive firefighting to proactive, self-optimizing operations built on trust and data integrity. 

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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 ...

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.