Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

At Civo Navigate London 2025, we hosted a panel discussion with Josh Mesout, James Faure, Abdul Hummaida, Jonas Vermeulen, and Daniel Miodovnik to discuss the latest trends and challenges in AI adoption. Through this conversation, the panelists covered topics such as the current state of AI adoption to the challenges of scaling AI, and the future of work.

AWS did not intend to build Amazon EKS; it simply had to. Kubernetes adoption beamed light years ahead of AWS’s own managed container orchestration service. This forced AWS to develop a managed service to accommodate customers who wanted to use upstream Kubernetes but did not want to manage it themselves. As soon as AWS got around to it, it knocked the Kubernetes-based container management service out of the park. Not only is Amazon EKS simpler than Kubernetes, but EKS pricing may also be worth it.

A test fails. You rerun it and it passes. You shrug and move on. This is how most teams deal with flaky tests. The “rerun until green” approach works in the moment, and rerunning from failed tests is a useful way to confirm whether a failure is real. But reruns don’t fix the underlying issue. Over time, they burn CI resources and can hide real instability in your code. On the other hand, fixing flaky tests can mean hours of work.