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ScienceLogic Introduces Skylar™ AI

ScienceLogic introduced Skylar™ AI.

Reasoning over telemetry and stored knowledge, Skylar delivers accurate predictions, tailored recommendations, and intelligent automations that drive business efficiency and innovation.

Skylar AI is an AI suite that harnesses the power of generative AI and unsupervised machine learning combined with human-in-the-loop automation training models to revolutionize IT operations. By automating complex troubleshooting tasks, Skylar unburdens human experts from spending time on routine operational tasks, freeing up more time for innovation. Skylar redefines what’s possible in IT operations (ITOps), paving the way for exceptional customer experiences and unprecedented business agility.

“We stand at the precipice of a new era," said Dave Link, CEO and co-founder of ScienceLogic. "The nonstop pace of technological innovation has created IT ecosystems of staggering complexity, far surpassing human capacity to manage effectively. Skylar AI is our answer to this monumental challenge. It's a paradigm shift. By harnessing the revolutionary potential of machine learning and generative AI, we're not merely solving today's IT problems—we're architecting the future of IT management. Skylar doesn't just react; it anticipates, learns, and evolves. This isn't incremental improvement; it's a quantum leap that will redefine how businesses operate in the relentless digital age, ensuring they don't just keep pace with innovation, but drive it."

Skylar AI can proactively uncover insights, curate data, and guide users to business-impacting issues before they happen. It moves past simply managing problems to predicting and preventing them before they occur by drawing from three primary components:

- Skylar Advisor: An AI advisor that transcends the limitations of today’s AI assistants, often known as co-pilots. It proactively delivers context-rich insights, precise predictions, and actionable recommendations, enabling businesses to prevent issues before they occur, optimize resources, and drive innovation without relying on constant human expertise. Skylar Advisor empowers users of all levels to easily align with institutional best practices to address IT issues efficiently.*

- Skylar Analytics: A set of intelligent unsupervised AI/ML analytics coupled with deep data exploration and visualization capabilities, allowing teams to rapidly reason over critical and historical data to inform and empower the business to make strategic decisions quickly and confidently.

- Skylar Automated Root Cause Analysis (RCA): Real-time root cause analysis for application and environment logs to quickly identify incident details, showing key log lines, plain language summaries, and recommended actions, saving hours (sometimes days) of manual effort.

*Some features to be available in late 2024.

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

ScienceLogic Introduces Skylar™ AI

ScienceLogic introduced Skylar™ AI.

Reasoning over telemetry and stored knowledge, Skylar delivers accurate predictions, tailored recommendations, and intelligent automations that drive business efficiency and innovation.

Skylar AI is an AI suite that harnesses the power of generative AI and unsupervised machine learning combined with human-in-the-loop automation training models to revolutionize IT operations. By automating complex troubleshooting tasks, Skylar unburdens human experts from spending time on routine operational tasks, freeing up more time for innovation. Skylar redefines what’s possible in IT operations (ITOps), paving the way for exceptional customer experiences and unprecedented business agility.

“We stand at the precipice of a new era," said Dave Link, CEO and co-founder of ScienceLogic. "The nonstop pace of technological innovation has created IT ecosystems of staggering complexity, far surpassing human capacity to manage effectively. Skylar AI is our answer to this monumental challenge. It's a paradigm shift. By harnessing the revolutionary potential of machine learning and generative AI, we're not merely solving today's IT problems—we're architecting the future of IT management. Skylar doesn't just react; it anticipates, learns, and evolves. This isn't incremental improvement; it's a quantum leap that will redefine how businesses operate in the relentless digital age, ensuring they don't just keep pace with innovation, but drive it."

Skylar AI can proactively uncover insights, curate data, and guide users to business-impacting issues before they happen. It moves past simply managing problems to predicting and preventing them before they occur by drawing from three primary components:

- Skylar Advisor: An AI advisor that transcends the limitations of today’s AI assistants, often known as co-pilots. It proactively delivers context-rich insights, precise predictions, and actionable recommendations, enabling businesses to prevent issues before they occur, optimize resources, and drive innovation without relying on constant human expertise. Skylar Advisor empowers users of all levels to easily align with institutional best practices to address IT issues efficiently.*

- Skylar Analytics: A set of intelligent unsupervised AI/ML analytics coupled with deep data exploration and visualization capabilities, allowing teams to rapidly reason over critical and historical data to inform and empower the business to make strategic decisions quickly and confidently.

- Skylar Automated Root Cause Analysis (RCA): Real-time root cause analysis for application and environment logs to quickly identify incident details, showing key log lines, plain language summaries, and recommended actions, saving hours (sometimes days) of manual effort.

*Some features to be available in late 2024.

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...