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ScienceLogic Releases Skylar AI 2.5

ScienceLogic announced Skylar AI 2.5, expanding secure deployment options for organizations with stringent security, sovereignty, and compliance requirements, while introducing enhancements that strengthen AI performance, operational intelligence, and enterprise integrations. 

The release further improves AI accuracy, platform performance, and natural language user experience across the ScienceLogic AI Platform.

Serving as the intelligence layer of the ScienceLogic AI Platform™, Skylar AI provides always-on guidance across complex IT environments, a critical capability for organizations operationalizing agentic AI. Organizations adopting AI often require infrastructure that aligns with strict security, sovereignty, and compliance requirements. Skylar AI 2.5 addresses those requirements by making its AI capabilities available through flexible deployment options, including sovereign cloud, on premises, and secure cloud environments.

"Organizations across government and other highly regulated industries are moving quickly to adopt AI, but they can't compromise on security, governance, or operational requirements," said Lee Koepping, VP of global sales engineering at ScienceLogic. "Skylar AI 2.5 gives customers the flexibility to deploy trusted AI in the environments that best meet their needs, whether that's sovereign, on premises, or secure cloud, while delivering the operational intelligence they need to confidently accelerate AI adoption."

In addition to deployment flexibility, Skylar AI 2.5 introduces numerous enhancements focused on performance, usability, and AI-driven operations, including:

  • Improve operational guidance: Enhanced advisory capabilities strengthen event matching, improve severity assignment, and streamline workflows through tighter integration with Skylar One™ alerts.
  • Increase trust in AI recommendations: Improved AI knowledge capabilities offer expanded enterprise content support and greater transparency across AI-generated outputs.
  • Gain deeper insight: Richer analytics and visualization enhancements provide more actionable insights through improved dashboards, filtering, trend analysis, and service visibility.
  • Scale AI operations more efficiently: Platform optimizations reduce execution time, improve responsiveness, and increase scalability.
  • Connect enterprise workflows: Enhanced support for Microsoft Teams, ServiceNow, and other enterprise workflows.
  • Strengthen AI governance: Monitor internal Skylar AI agents, track token usage over time, and turn agents on or off as operational requirements change.

"As organizations move from experimenting with AI to operationalizing it, success depends on more than the model itself. It requires trusted operational data, flexible deployment options, and governance that allows AI to operate confidently in real-world environments," said Michael Nappi, chief product officer at ScienceLogic. "Skylar AI 2.5 brings those capabilities together so organizations can securely scale AI across even their most demanding IT operations."

The Skylar AI 2.5 release builds on recent enhancements to Skylar Analytics, Skylar Advisor, and the Skylar One “Kyoto” release, further advancing ScienceLogic’s mission to help organizations implement AI-powered IT operations with greater governance, automation, and intelligence. The release also reinforces ScienceLogic’s commitment to delivering secure, scalable AI solutions for organizations operating in highly regulated environments, building on the company’s FedRAMP® Moderate authorization achieved last year. 

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

ScienceLogic Releases Skylar AI 2.5

ScienceLogic announced Skylar AI 2.5, expanding secure deployment options for organizations with stringent security, sovereignty, and compliance requirements, while introducing enhancements that strengthen AI performance, operational intelligence, and enterprise integrations. 

The release further improves AI accuracy, platform performance, and natural language user experience across the ScienceLogic AI Platform.

Serving as the intelligence layer of the ScienceLogic AI Platform™, Skylar AI provides always-on guidance across complex IT environments, a critical capability for organizations operationalizing agentic AI. Organizations adopting AI often require infrastructure that aligns with strict security, sovereignty, and compliance requirements. Skylar AI 2.5 addresses those requirements by making its AI capabilities available through flexible deployment options, including sovereign cloud, on premises, and secure cloud environments.

"Organizations across government and other highly regulated industries are moving quickly to adopt AI, but they can't compromise on security, governance, or operational requirements," said Lee Koepping, VP of global sales engineering at ScienceLogic. "Skylar AI 2.5 gives customers the flexibility to deploy trusted AI in the environments that best meet their needs, whether that's sovereign, on premises, or secure cloud, while delivering the operational intelligence they need to confidently accelerate AI adoption."

In addition to deployment flexibility, Skylar AI 2.5 introduces numerous enhancements focused on performance, usability, and AI-driven operations, including:

  • Improve operational guidance: Enhanced advisory capabilities strengthen event matching, improve severity assignment, and streamline workflows through tighter integration with Skylar One™ alerts.
  • Increase trust in AI recommendations: Improved AI knowledge capabilities offer expanded enterprise content support and greater transparency across AI-generated outputs.
  • Gain deeper insight: Richer analytics and visualization enhancements provide more actionable insights through improved dashboards, filtering, trend analysis, and service visibility.
  • Scale AI operations more efficiently: Platform optimizations reduce execution time, improve responsiveness, and increase scalability.
  • Connect enterprise workflows: Enhanced support for Microsoft Teams, ServiceNow, and other enterprise workflows.
  • Strengthen AI governance: Monitor internal Skylar AI agents, track token usage over time, and turn agents on or off as operational requirements change.

"As organizations move from experimenting with AI to operationalizing it, success depends on more than the model itself. It requires trusted operational data, flexible deployment options, and governance that allows AI to operate confidently in real-world environments," said Michael Nappi, chief product officer at ScienceLogic. "Skylar AI 2.5 brings those capabilities together so organizations can securely scale AI across even their most demanding IT operations."

The Skylar AI 2.5 release builds on recent enhancements to Skylar Analytics, Skylar Advisor, and the Skylar One “Kyoto” release, further advancing ScienceLogic’s mission to help organizations implement AI-powered IT operations with greater governance, automation, and intelligence. The release also reinforces ScienceLogic’s commitment to delivering secure, scalable AI solutions for organizations operating in highly regulated environments, building on the company’s FedRAMP® Moderate authorization achieved last year. 

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...