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ScienceLogic Rolls Out Monitoring and Dependency Mapping for IBM SoftLayer

ScienceLogic announced complete monitoring and dependency mapping for IBM SoftLayer.

ScienceLogic gives customers a single monitoring view across multiple cloud providers including Amazon Web Services (AWS), Microsoft Azure, VMware vCloud Air, in addition to all on-premises IT elements.

ScienceLogic gives customers a total view of their SoftLayer cloud, before, during, and after transition to the cloud. Additionally, customers can:

- Discover infrastructure elements placed in SoftLayer, including account, region, datacenter, virtual, and bare metal servers, networks, load balancers, and storage

- Visually map the relationships between them automatically in real time

- Monitor availability of compute and storage resources

- Create real-time dashboards showing availability and performance of compute, storage, and other resources in SoftLayer

- Monitor virtual server performance (CPU, memory, disk usage)

- Collect detail on load balancer and bare metal server configuration and status

- Collect and manage events from all SoftLayer elements

- Track spending with a summary of your account invoice

- View interdependency of resources across multiple public and private clouds

“Our customers have globally distributed infrastructures and we have seen their need to leverage multiple cloud providers grow over time,” said Dave Link, CEO ScienceLogic. “They need a simple, consolidated way to evaluate the performance of these investments, control costs and address service delivery challenges common to globally distributed infrastructures. In addition to those other leading cloud platforms we support, it made perfect sense to add IBM SoftLayer to the list.”

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ScienceLogic Rolls Out Monitoring and Dependency Mapping for IBM SoftLayer

ScienceLogic announced complete monitoring and dependency mapping for IBM SoftLayer.

ScienceLogic gives customers a single monitoring view across multiple cloud providers including Amazon Web Services (AWS), Microsoft Azure, VMware vCloud Air, in addition to all on-premises IT elements.

ScienceLogic gives customers a total view of their SoftLayer cloud, before, during, and after transition to the cloud. Additionally, customers can:

- Discover infrastructure elements placed in SoftLayer, including account, region, datacenter, virtual, and bare metal servers, networks, load balancers, and storage

- Visually map the relationships between them automatically in real time

- Monitor availability of compute and storage resources

- Create real-time dashboards showing availability and performance of compute, storage, and other resources in SoftLayer

- Monitor virtual server performance (CPU, memory, disk usage)

- Collect detail on load balancer and bare metal server configuration and status

- Collect and manage events from all SoftLayer elements

- Track spending with a summary of your account invoice

- View interdependency of resources across multiple public and private clouds

“Our customers have globally distributed infrastructures and we have seen their need to leverage multiple cloud providers grow over time,” said Dave Link, CEO ScienceLogic. “They need a simple, consolidated way to evaluate the performance of these investments, control costs and address service delivery challenges common to globally distributed infrastructures. In addition to those other leading cloud platforms we support, it made perfect sense to add IBM SoftLayer to the list.”

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As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...