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LogicMonitor Enhances LM Envision Platform and Introduces Dexda

LogicMonitor announced expanded integrations, insights and workflows to the LM Envision Platform.

LogicMonitor is also introducing Dexda, an event management solution that filters through the noise of thousands of daily alerts by using advanced machine learning (ML) techniques, contextual enrichment capabilities and deduplication efforts. Together, these additions allow customers to reach a significantly lower mean time to resolution and lower risks to the business.

“Every business is under tremendous pressure to seamlessly deliver exceptional digital performance,” states Christina Kosmowski, CEO, LogicMonitor. “To efficiently do that, our customers look to us to contextualize the overwhelming amount of data within their complex IT environments.“

The core of LogicMonitor’s platform has been built with advanced machine learning, intelligence and automation, combined to abstract complexity and deliver business impact through IT data collaboration. The company has focused its product roadmap in the areas of intelligence, experience and extensibility.

Dexda adds AIOps built on top of LogicMonitor’s extensive data set and integrated into its platform, so users can effortlessly move from alerting to automating actions.

Key attributes of Dexda include:

- Adaptive Correlation - Alerts are automatically re-clustered when a more optimal option is detected.

- ServiceNow Ready - Automatically enriches Dexda alerts with ServiceNow CMDB data to drive additional context for ML correlations.

- User-defined Correlation - Dexda admins can now fine-tune the ML models to meet their unique needs or build new ML models.

In addition to Dexda, LogicMonitor has also delivered:

- Event-Driven Ansible Integration - This jointly developed solution with Red Hat assists with auto-remediation and auto-troubleshooting. This integration lets customers trigger remediation workflows in Ansible and act in accordance with predefined rules.

- Datapoint Analysis - Leverages machine learning techniques to find related metrics and patterns across different resources, which in turn reduces MTTR and increases productivity.

A unified platform experience is critical for consistency, adaptability and scalability while reducing tool sprawl and data complexity. Troubleshooting in hybrid modern environments requires a contextual and intuitive UX across devices, services and networks. This modernization and unification effort is the key to continually delivering new capabilities to users and keeping time to value short for new customers.

- UI Modernization - Optimized to present information in complex hybrid environments. Components for all parts of the LM Envision platform now include bulk actions, better search and filtering and new editors for LogicModules.

- Expanded Cloud Support - 20 new out-of-the-box dashboards for AWS and Azure, accelerating time to value while providing service-specific views for more insight into health, performance and availability.

- Log Ingest and Filter Simplification - Introduced declarative UI to simplify log collection and configuration. Users can also add custom LM Properties to the logs which allows for more flexible searching and potentially faster MTTR.

- Digital Experience Monitoring - Synthetic tests now support multi-factor authentication (MFA) and automated alerts for latency and error conditions.

As a trusted partner in the advancement of monitoring across on-prem, hybrid and cloud environments, LogicMonitor continues to invest in new ways to manage and monitor network equipment through integrations woven tightly into its overall platform experience.

- Improved VMware vSphere Support - Support for vSphere 8 and automation for the discovery and monitoring of new ESXi Hosts and mission-critical Virtual Machines, eliminating manual processes – reducing the time, resources and risk involved in repeatable remediation processes.

- Cisco Meraki and Catalyst SD-WAN - These new integrations make it easier than ever to monitor Cisco environments in the broader context of one's heterogenous hybrid infrastructure. Customers can now get alerted about anomalous events, visualize network traffic usage and see how Cisco vEdge/cEdge (formerly Viptela), SD-WAN Controllers, Meraki Security Appliances, Switches, Wireless Access Points and Smart Cameras connect to their network and where alert conditions exist.

- Improved Kubernetes Monitoring - Greater coverage and deeper visibility into frequently changing cloud environments with new support and coverage for Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere and enhanced Kubernetes helm and scheduler monitoring.

- SaaS Monitoring - M365 and Okta logs allow users to clearly understand why problems happen, pinpoint the root cause and quickly troubleshoot alongside alerts.

By advancing many key features of its platform, LogicMonitor customers can harness the full potential of their data to make informed decisions with confidence and efficiency. This approach not only streamlines operations, but also provides clarity and precision to the complexities of their IT landscape.

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 ...

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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.

LogicMonitor Enhances LM Envision Platform and Introduces Dexda

LogicMonitor announced expanded integrations, insights and workflows to the LM Envision Platform.

LogicMonitor is also introducing Dexda, an event management solution that filters through the noise of thousands of daily alerts by using advanced machine learning (ML) techniques, contextual enrichment capabilities and deduplication efforts. Together, these additions allow customers to reach a significantly lower mean time to resolution and lower risks to the business.

“Every business is under tremendous pressure to seamlessly deliver exceptional digital performance,” states Christina Kosmowski, CEO, LogicMonitor. “To efficiently do that, our customers look to us to contextualize the overwhelming amount of data within their complex IT environments.“

The core of LogicMonitor’s platform has been built with advanced machine learning, intelligence and automation, combined to abstract complexity and deliver business impact through IT data collaboration. The company has focused its product roadmap in the areas of intelligence, experience and extensibility.

Dexda adds AIOps built on top of LogicMonitor’s extensive data set and integrated into its platform, so users can effortlessly move from alerting to automating actions.

Key attributes of Dexda include:

- Adaptive Correlation - Alerts are automatically re-clustered when a more optimal option is detected.

- ServiceNow Ready - Automatically enriches Dexda alerts with ServiceNow CMDB data to drive additional context for ML correlations.

- User-defined Correlation - Dexda admins can now fine-tune the ML models to meet their unique needs or build new ML models.

In addition to Dexda, LogicMonitor has also delivered:

- Event-Driven Ansible Integration - This jointly developed solution with Red Hat assists with auto-remediation and auto-troubleshooting. This integration lets customers trigger remediation workflows in Ansible and act in accordance with predefined rules.

- Datapoint Analysis - Leverages machine learning techniques to find related metrics and patterns across different resources, which in turn reduces MTTR and increases productivity.

A unified platform experience is critical for consistency, adaptability and scalability while reducing tool sprawl and data complexity. Troubleshooting in hybrid modern environments requires a contextual and intuitive UX across devices, services and networks. This modernization and unification effort is the key to continually delivering new capabilities to users and keeping time to value short for new customers.

- UI Modernization - Optimized to present information in complex hybrid environments. Components for all parts of the LM Envision platform now include bulk actions, better search and filtering and new editors for LogicModules.

- Expanded Cloud Support - 20 new out-of-the-box dashboards for AWS and Azure, accelerating time to value while providing service-specific views for more insight into health, performance and availability.

- Log Ingest and Filter Simplification - Introduced declarative UI to simplify log collection and configuration. Users can also add custom LM Properties to the logs which allows for more flexible searching and potentially faster MTTR.

- Digital Experience Monitoring - Synthetic tests now support multi-factor authentication (MFA) and automated alerts for latency and error conditions.

As a trusted partner in the advancement of monitoring across on-prem, hybrid and cloud environments, LogicMonitor continues to invest in new ways to manage and monitor network equipment through integrations woven tightly into its overall platform experience.

- Improved VMware vSphere Support - Support for vSphere 8 and automation for the discovery and monitoring of new ESXi Hosts and mission-critical Virtual Machines, eliminating manual processes – reducing the time, resources and risk involved in repeatable remediation processes.

- Cisco Meraki and Catalyst SD-WAN - These new integrations make it easier than ever to monitor Cisco environments in the broader context of one's heterogenous hybrid infrastructure. Customers can now get alerted about anomalous events, visualize network traffic usage and see how Cisco vEdge/cEdge (formerly Viptela), SD-WAN Controllers, Meraki Security Appliances, Switches, Wireless Access Points and Smart Cameras connect to their network and where alert conditions exist.

- Improved Kubernetes Monitoring - Greater coverage and deeper visibility into frequently changing cloud environments with new support and coverage for Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere and enhanced Kubernetes helm and scheduler monitoring.

- SaaS Monitoring - M365 and Okta logs allow users to clearly understand why problems happen, pinpoint the root cause and quickly troubleshoot alongside alerts.

By advancing many key features of its platform, LogicMonitor customers can harness the full potential of their data to make informed decisions with confidence and efficiency. This approach not only streamlines operations, but also provides clarity and precision to the complexities of their IT landscape.

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.