Operations | Monitoring | ITSM | DevOps | Cloud

The recent AWS outage exposed how fragile the internet remains. Amazon traced the hours-long disruption to a DNS error—a small failure with massive reach. For most organizations, DNS operates quietly in the background. When it fails, every digital service connected to it stops. One of LogicMonitor’s valued customers, IG Group, faced a similar event less than ten hours after enabling Edwin AI.

Like many others, as a company, we are going through a steep learning exercise with coding agents. As we are all learning together how to face the challenges of this new era of coding, I wanted to share our learnings with you and, in turn, benefit from what you have learned in your journey with using coding agents.

Inventory management is at the heart of every organization’s operations. Yet many companies still rely on Excel files or manual processes to track their equipment, tools, or materials. These outdated methods often lead to errors, losses, and a lack of visibility. Today, inventory management software with a mobile app and barcode system allows you to centralize and automate asset management while making operations faster and more reliable.

The scale of modern IT is outpacing human capacity. Microservices, multi-cloud deployments, and the Internet of Things (IoT) have created a complex IT ecosystem, generating an exponential volume of operational data. Traditional operations teams, regardless of their skill, struggle to keep up. Because of this, forward-looking leaders are adopting AIOps in IT, not as an upgrade, but as a foundational shift.

In my previous blog, we demonstrated how the FrogML SDK streamlines the process of integrating custom-built or publicly sourced models from your IDE into JFrog Artifactory. Now that your models are securely stored, versioned, and managed, the natural next question arises: “Ok, so you have some models in JFrog Artifactory, now what?” This is where the real power of the JFrog Platform comes into play.

The challenge of storing, processing, and alerting on your time series data is only part of the battle when it comes to deriving value from time-stamped data. While InfluxDB 3 addresses those hurdles with the database and Python processing engine, data analytics teams still need to be able to visualize their data and build dashboards to complete the time series story.

In a microservices architecture, a single user request can pass through multiple services before completing. When performance drops or an error occurs, tracing that journey is the only way to locate the source. Distributed tracing provides that visibility. At its core are OpenTelemetry Spans — units of work that capture what each service does during a request.

Modern software applications are increasingly complex. Microservices, cloud infrastructure, and distributed architectures make it challenging for developers, DevOps engineers, and SREs to maintain high performance and a seamless user experience. Traditional Application Performance Monitoring (APM) provides critical insights into how applications perform, but alone, it often leaves blind spots when it comes to diagnosing issues or understanding the full system behavior.

The engineer you pay $200,000 a year just spent an hour copy-pasting data between dashboards. Again. Software engineers have critical skills that are in the highest demand. And yet, many world-class engineers are currently spending too much of their time clearing tickets, routing alerts, and responding to the same types of incidents over and over again. This operational toil is costing you.

The best way to minimize the impact of an incident is to catch it early, before small issues snowball into major disruptions. That requires maintaining healthy systems and ensuring sufficient resources are available when problems arise. But developers and IT operations pros working in large enterprises face a challenge: Complex systems operate in an inherently degraded state. In his essay “How Complex Systems Fail,” Dr.