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LogicMonitor Collaborates with IBM and Red Hat on Autonomous Data Centers

LogicMonitor announced a collaboration with IBM to integrate IBM watsonx and Red Hat Ansible Automation Platform with automation coding assistant into Edwin AI, LogicMonitor’s AI Agent for IT Operations. 

This integration is designed to help customers scale autonomous IT operations by proactively detecting, diagnosing, and resolving issues before they impact service reliability.

LogicMonitor’s Edwin AI helps customers identify anomalies and, together with Red Hat Ansible Automation Platform, translate insights into action. When an issue arises, Edwin AI uses Ansible Automation Platform to recommend or trigger the appropriate Ansible Playbook. If no playbook exists, the automation coding assistant within Red Hat Ansible Automation Platform and IBM watsonx automatically generate one to help troubleshoot or restore services automatically. All of this can be achieved by the joint solution for accelerated reaction and response times. Once identified, automation is orchestrated via Red Hat Ansible Automation Platform, enabling IT teams to control and approve it as trusted automation before it executes. Additionally, automation can be scaled across teams without specialized training.

“This is automation with frontier intelligence,” said Karthik SJ, General Manager of AI, LogicMonitor. “By combining LogicMonitor’s AI-powered observability with Red Hat Ansible Automation Platform and watsonx, we can offer customers a trusted ally designed to anticipate and resolve issues autonomously, freeing teams to focus on building the future.”

The integration is designed to deliver less firefighting and more innovation. Enterprises can gain confidence from predictive resilience, efficient incident response, and the ability to accelerate self-healing across hybrid and multi-cloud estates, which is expected to drive these benefits:

  • Stop incidents before they happen: Edwin AI identifies abnormal behavior across infrastructure, helping teams prevent outages before they affect critical services.
  • Fix problems automatically, not manually: The system is designed to resolve incidents from detection to remediation, reducing handoffs and on-call fatigue.
  • Recover faster when incidents occur: Automated resolution shortens response times during outages, helping keep business-critical services online and minimizing disruption.
  • Extend automation to more teams and environments: Teams can standardize self-healing workflows across hybrid and multi-cloud environments without relying on deep automation expertise.

“Logic Monitor’s Edwin AI platform combined with IBM watsonx and Red Hat Ansible Automation Platform enables automated prevention, detection, and remediation of infrastructure issues to help enterprises achieve less down time and more efficient enterprise IT operations,” said Nick Holda, Vice President, AI Technology Partnerships at IBM. “This is a great example of how IBM and our ecosystem partners can bring together complimentary technologies to offer new end-to-end solutions to our clients."

LogicMonitor continues to innovate with IBM’s watsonx suite of products to offer solutions designed to help enterprises anticipate, automate, and renew their operations in an increasingly complex digital era. 

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LogicMonitor Collaborates with IBM and Red Hat on Autonomous Data Centers

LogicMonitor announced a collaboration with IBM to integrate IBM watsonx and Red Hat Ansible Automation Platform with automation coding assistant into Edwin AI, LogicMonitor’s AI Agent for IT Operations. 

This integration is designed to help customers scale autonomous IT operations by proactively detecting, diagnosing, and resolving issues before they impact service reliability.

LogicMonitor’s Edwin AI helps customers identify anomalies and, together with Red Hat Ansible Automation Platform, translate insights into action. When an issue arises, Edwin AI uses Ansible Automation Platform to recommend or trigger the appropriate Ansible Playbook. If no playbook exists, the automation coding assistant within Red Hat Ansible Automation Platform and IBM watsonx automatically generate one to help troubleshoot or restore services automatically. All of this can be achieved by the joint solution for accelerated reaction and response times. Once identified, automation is orchestrated via Red Hat Ansible Automation Platform, enabling IT teams to control and approve it as trusted automation before it executes. Additionally, automation can be scaled across teams without specialized training.

“This is automation with frontier intelligence,” said Karthik SJ, General Manager of AI, LogicMonitor. “By combining LogicMonitor’s AI-powered observability with Red Hat Ansible Automation Platform and watsonx, we can offer customers a trusted ally designed to anticipate and resolve issues autonomously, freeing teams to focus on building the future.”

The integration is designed to deliver less firefighting and more innovation. Enterprises can gain confidence from predictive resilience, efficient incident response, and the ability to accelerate self-healing across hybrid and multi-cloud estates, which is expected to drive these benefits:

  • Stop incidents before they happen: Edwin AI identifies abnormal behavior across infrastructure, helping teams prevent outages before they affect critical services.
  • Fix problems automatically, not manually: The system is designed to resolve incidents from detection to remediation, reducing handoffs and on-call fatigue.
  • Recover faster when incidents occur: Automated resolution shortens response times during outages, helping keep business-critical services online and minimizing disruption.
  • Extend automation to more teams and environments: Teams can standardize self-healing workflows across hybrid and multi-cloud environments without relying on deep automation expertise.

“Logic Monitor’s Edwin AI platform combined with IBM watsonx and Red Hat Ansible Automation Platform enables automated prevention, detection, and remediation of infrastructure issues to help enterprises achieve less down time and more efficient enterprise IT operations,” said Nick Holda, Vice President, AI Technology Partnerships at IBM. “This is a great example of how IBM and our ecosystem partners can bring together complimentary technologies to offer new end-to-end solutions to our clients."

LogicMonitor continues to innovate with IBM’s watsonx suite of products to offer solutions designed to help enterprises anticipate, automate, and renew their operations in an increasingly complex digital era. 

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...