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ScienceLogic Announces Kyoto Release of Skylar One

ScienceLogic announced the “Kyoto” release of Skylar One, the core observability offering of the ScienceLogic AI Platform™. 

The Kyoto release provides new geographic service visibility, simplified location and device management, improved relationship context, and platform modernization designed to help IT teams operate with greater speed, confidence, and control.

Skylar One’s Kyoto update helps enterprises manage increasingly complex IT environments spanning hybrid infrastructure, cloud environments, and AI-driven operations. These enhancements provide clearer service context, faster service investigations, simplified location and access management, and improved platform resilience and scalability.

Highlights from the Kyoto release include:

  • Context-rich geographic service visibility: New Geographic Service Maps provide an interactive view of service health, availability, and risk across locations, helping teams assess business impact faster and prioritize response efforts.
  • Faster, cleaner service investigations: Enhancements to Business Services Investigator 2.0 automatically collapse Skylar AI and change swimlanes when no relevant events exist in the viewed time window, keeping the interface focused on active signals. Users can manually expand or collapse these lanes at any time, helping operators spend less time filtering out noise and more time investigating the signals that matter.
  • Simplified location management: Updated location management capabilities make it easier to organize devices and services by location, improving operational visibility while reducing administrative overhead.
  • Enhanced device management and relationship context: Expanded AP2™ device management capabilities and improved relationship visualization give teams a clearer view of dependencies, helping shorten investigations, streamline workflows, and troubleshoot with greater confidence.
  • Centralized user and access management: Global Manager now supports centralized management of user accounts, access keys, organizations, and user policies across managed stacks through API access, helping service providers and enterprise teams reduce administrative overhead, enforce consistent access policies, and improve operational efficiency and security posture.
  • Greater platform resilience and scalability: Upgrades to the underlying database, cloud infrastructure, high-availability architecture, and runtime components strengthen the stability, security, and scalability required for modern hybrid environments.
  • More secure automation and integration workflows: New API key authentication strengthens control over integrations, automation workflows, and third-party applications, helping organizations modernize API security while preserving operational flexibility.

“As enterprises move from visibility toward AI-assisted and increasingly autonomous operations, they need more than alerts. They need trusted service context that shows what is happening, where it is happening, and what it means for the business,” said Michael Nappi, chief product officer at ScienceLogic. “The Kyoto release strengthens Skylar One by giving customers a more accurate understanding of relationships across services, devices, locations, and access policies while modernizing the operational foundation they need to investigate faster and automate with confidence.”

As the core observability offering of the ScienceLogic AI Platform, Skylar One, together with Skylar AI, Skylar Automation, and Skylar Compliance, delivers deeper service-aware operational intelligence across complex hybrid environments. The Kyoto release builds on recent advancements, including Skylar One “Juneau” and the launch of Skylar™ Advisor, further advancing ScienceLogic's commitment to AI-powered IT operations by helping organizations move from fragmented visibility to decision-ready insights and governed automation.

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For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

ScienceLogic Announces Kyoto Release of Skylar One

ScienceLogic announced the “Kyoto” release of Skylar One, the core observability offering of the ScienceLogic AI Platform™. 

The Kyoto release provides new geographic service visibility, simplified location and device management, improved relationship context, and platform modernization designed to help IT teams operate with greater speed, confidence, and control.

Skylar One’s Kyoto update helps enterprises manage increasingly complex IT environments spanning hybrid infrastructure, cloud environments, and AI-driven operations. These enhancements provide clearer service context, faster service investigations, simplified location and access management, and improved platform resilience and scalability.

Highlights from the Kyoto release include:

  • Context-rich geographic service visibility: New Geographic Service Maps provide an interactive view of service health, availability, and risk across locations, helping teams assess business impact faster and prioritize response efforts.
  • Faster, cleaner service investigations: Enhancements to Business Services Investigator 2.0 automatically collapse Skylar AI and change swimlanes when no relevant events exist in the viewed time window, keeping the interface focused on active signals. Users can manually expand or collapse these lanes at any time, helping operators spend less time filtering out noise and more time investigating the signals that matter.
  • Simplified location management: Updated location management capabilities make it easier to organize devices and services by location, improving operational visibility while reducing administrative overhead.
  • Enhanced device management and relationship context: Expanded AP2™ device management capabilities and improved relationship visualization give teams a clearer view of dependencies, helping shorten investigations, streamline workflows, and troubleshoot with greater confidence.
  • Centralized user and access management: Global Manager now supports centralized management of user accounts, access keys, organizations, and user policies across managed stacks through API access, helping service providers and enterprise teams reduce administrative overhead, enforce consistent access policies, and improve operational efficiency and security posture.
  • Greater platform resilience and scalability: Upgrades to the underlying database, cloud infrastructure, high-availability architecture, and runtime components strengthen the stability, security, and scalability required for modern hybrid environments.
  • More secure automation and integration workflows: New API key authentication strengthens control over integrations, automation workflows, and third-party applications, helping organizations modernize API security while preserving operational flexibility.

“As enterprises move from visibility toward AI-assisted and increasingly autonomous operations, they need more than alerts. They need trusted service context that shows what is happening, where it is happening, and what it means for the business,” said Michael Nappi, chief product officer at ScienceLogic. “The Kyoto release strengthens Skylar One by giving customers a more accurate understanding of relationships across services, devices, locations, and access policies while modernizing the operational foundation they need to investigate faster and automate with confidence.”

As the core observability offering of the ScienceLogic AI Platform, Skylar One, together with Skylar AI, Skylar Automation, and Skylar Compliance, delivers deeper service-aware operational intelligence across complex hybrid environments. The Kyoto release builds on recent advancements, including Skylar One “Juneau” and the launch of Skylar™ Advisor, further advancing ScienceLogic's commitment to AI-powered IT operations by helping organizations move from fragmented visibility to decision-ready insights and governed automation.

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...