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ScienceLogic Incorporates Acquired AppFirst Technology into Latest Release

ScienceLogic announced a new release of its software platform, the first release to incorporate technology from the company’s acquisition of AppFirst in August 2016.

ScienceLogic customers can now discover, monitor, and alert on transient IT infrastructure components that support modern application designs.

“We’ve seen a paradigm shift in our industry towards microservice-based applications,” said Dave Link, CEO, ScienceLogic. “It presents significant challenges for IT staff responsible for ensuring service delivery. Virtual machines and application processes are automatically provisioned and de-provisioned so fast that they’re never detected or monitored. This makes service assurance a daunting task.”

To meet this challenge, ScienceLogic has introduced lightweight agent technology. The agents allow for automated discovery of short-lived compute instances, providing support for virtually any type of elastic or microservice-based application, without the need for third party API integration. This means better service assurance coverage for the next wave of dynamic applications.

“Real service assurance for modern applications is now a reality, by combining agent and agentless monitoring technologies in a single unified platform,” said Link. “The core platform is agentless, which means significantly less administrative overhead. But when customers require the collection of incredibly granular metrics in specific situations, ScienceLogic can accommodate their needs.”

ScienceLogic’s new agent technology also allows customers to monitor and analyze log data that often holds the key to pinpointing the cause of application and infrastructure performance issues.

Other highlights of the release include:

- Agent-Based Monitoring: ScienceLogic’s lightweight, patented agent technology allows customers to discover and monitor short lived IT workloads that would be missed with traditional poll-based techniques. No agent configuration files are required and everything can be managed from one central location.

- Log Analytics: ScienceLogic can generate actionable events directly from collected and filtered log events, with one log policy driving multiple event policies. Includes support for Linux OS log collection (RedHat, Oracle, Ubuntu, and Debian) as well as Windows local and event log collection (Windows 2008, 2012, Windows 7 and 8)

- Advanced Azure Cloud Assurance: ScienceLogic has expanded its coverage of Microsoft’s cloud with the ability to discover regions, resource groups, VMs, storage, virtual networks, Active Directory, Traffic Manager, and SQL databases in Azure environments. Customers may also view services grouped by region. Includes support for Azure Classic 3.4 and Azure Resource Manager (ARM).

- Scalable Platform Automation: ScienceLogic has increased scalability of its Runbook Automation engine by up to 500%. This means customers can now automate more workflows and reduce or even eliminate the cost of manually remediating and acknowledging IT performance issues.

“Many IT organizations are challenged with the ever increasing demand for more agile and higher quality service delivery at a lower cost,” said Link. “In response, we’re excited to offer our customers new capabilities that allow them to deliver on this goal.”

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ScienceLogic Incorporates Acquired AppFirst Technology into Latest Release

ScienceLogic announced a new release of its software platform, the first release to incorporate technology from the company’s acquisition of AppFirst in August 2016.

ScienceLogic customers can now discover, monitor, and alert on transient IT infrastructure components that support modern application designs.

“We’ve seen a paradigm shift in our industry towards microservice-based applications,” said Dave Link, CEO, ScienceLogic. “It presents significant challenges for IT staff responsible for ensuring service delivery. Virtual machines and application processes are automatically provisioned and de-provisioned so fast that they’re never detected or monitored. This makes service assurance a daunting task.”

To meet this challenge, ScienceLogic has introduced lightweight agent technology. The agents allow for automated discovery of short-lived compute instances, providing support for virtually any type of elastic or microservice-based application, without the need for third party API integration. This means better service assurance coverage for the next wave of dynamic applications.

“Real service assurance for modern applications is now a reality, by combining agent and agentless monitoring technologies in a single unified platform,” said Link. “The core platform is agentless, which means significantly less administrative overhead. But when customers require the collection of incredibly granular metrics in specific situations, ScienceLogic can accommodate their needs.”

ScienceLogic’s new agent technology also allows customers to monitor and analyze log data that often holds the key to pinpointing the cause of application and infrastructure performance issues.

Other highlights of the release include:

- Agent-Based Monitoring: ScienceLogic’s lightweight, patented agent technology allows customers to discover and monitor short lived IT workloads that would be missed with traditional poll-based techniques. No agent configuration files are required and everything can be managed from one central location.

- Log Analytics: ScienceLogic can generate actionable events directly from collected and filtered log events, with one log policy driving multiple event policies. Includes support for Linux OS log collection (RedHat, Oracle, Ubuntu, and Debian) as well as Windows local and event log collection (Windows 2008, 2012, Windows 7 and 8)

- Advanced Azure Cloud Assurance: ScienceLogic has expanded its coverage of Microsoft’s cloud with the ability to discover regions, resource groups, VMs, storage, virtual networks, Active Directory, Traffic Manager, and SQL databases in Azure environments. Customers may also view services grouped by region. Includes support for Azure Classic 3.4 and Azure Resource Manager (ARM).

- Scalable Platform Automation: ScienceLogic has increased scalability of its Runbook Automation engine by up to 500%. This means customers can now automate more workflows and reduce or even eliminate the cost of manually remediating and acknowledging IT performance issues.

“Many IT organizations are challenged with the ever increasing demand for more agile and higher quality service delivery at a lower cost,” said Link. “In response, we’re excited to offer our customers new capabilities that allow them to deliver on this goal.”

The Latest

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.