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.
The Latest
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
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 ...