LogicMonitor’s LM Envision observability platform places a strong focus on delivering the four pillars of AI-driven hybrid observability for any combination of application architectures and infrastructures on-premises, in colocation facilities, in the public cloud, or at edge locations. At its core, this ability to instantly adjust to complex brownfield environments is a direct result of LogicMonitor’s experience in building interfaces and connectors to integrate with everything from monolithic applications tied to mainframe hardware all the way to distributed microservices applications running on multiple Kubernetes clusters at different data centers, clouds, or edge locations. LogicMonitor’s 3,000+ integrations are the true star of the show, as they ensure the consistent collection of relevant telemetry data, the creation of service incidents, and, ultimately, the triggering of automated remediation and resolution workflows.

LogicMonitor introduced solutions that started with network and systems metrics and event collection many years ago. Along the way, it has expanded its platform to include log analytics (via acquisition of Unamoly), application health (via acquisition of Airbrake), and, most recently, AI analytics (via acquisition of Dexda). Importantly, LogicMonitor hasn’t just strapped these components to its base platform via duct tape and string; rather, it has taken the time to thoughtfully and thoroughly integrate them into its observability platform, resulting in a seamless experience for operators.

Some of LogicMonitor’s latest extensions include the Resource Explorer and Service Insights features, which enable easy organization of observability data related to individual services, applications, and locations. These capabilities help focus IT operations teams on business-critical applications and infrastructure, so they can better communicate with line-of-business teams and nontechnical leadership as to the state of the IT environment. For example, if a critical e-commerce app experiences an unusual decrease in transaction volume, Service Insight can instantly connect the issue to a database service running in a specific cloud region. This enables corporate IT to alert stakeholders immediately and offer database admins actionable insights to quickly resolve the issue, while updating nontechnical leadership on the potential revenue impact and expected timeframe to resolution.

While LogicMonitor has been applying AI technologies to hybrid observability for years via features such as metric anomaly detection, dynamic thresholding, and pattern recognition, the addition of the Dexda large language model AI engine, now known as Edwin AI, marked an important turning point. Launched in June 2024, Edwin AI has high potential for a truly transformative impact due to its use with an established observability platform, LM Envision, that provides the four pillars described above.

LogicMonitor has first applied Edwin AI to the task of correlating and reducing the often-overwhelming volume of events that comes along with true and proper hybrid observability. As part of correlation, Edwin AI generates insights and points to relevant relationships across data types and source domains, helping accelerate causal analysis and root isolation.

And the results have been impressive. Multiple customers are seeing event reduction rates of more than 80%, enabling operations teams to focus on what matters instead of digging through piles and piles of events to find the important and relevant indicators. MSPs using the LM Envision platform to manage customer environments have seen that Level 1 operators can deal with more issues, reducing escalations and enabling more senior staff to remain focused on value-add projects.

There are future plans, of course, to further expand the ways in which Edwin AI is applied to the various tasks facing hybrid operations and to add the capability to identify and trigger automated corrective responses. LogicMonitor is not rushing to push these out, however. Rather, the company is taking a measured approach to ensure that such capabilities are effective, accurate, and capable of adapting to the unique characteristics of each customer environment.