Observe Adds Tool for Exploring Kubernetes Clusters
Observe, Inc. today at the KubeCon + CloudNativeCon 2024 conference launched a tool that makes it possible to troubleshoot Kubernetes environments using metrics collected by the company’s namesake observability platform.
Rather than having to set up their own dashboards for visualizing Kubernetes clusters, the Kubernetes Explorer tool makes it possible to pinpoint the root cause of issues in a way that ultimately serves to improve meantime-to-resolution.
Observe CEO Jeremy Burton said, in effect, Kubernetes Explorer at no additional cost in effect addresses that last mile of observability that many IT teams otherwise would have needed to address themselves by building custom dashboards.
Kubernetes Explorer is an extension of a tool that Observe launched last September that makes use of generative artificial intelligence (AI) agents, dubbed AI Investigator, that makes it possible to query the Observe platform and automate tasks using natural language. Kubernetes Explorer can then be used to create custom, incident-specific visualizations and suggestions to optimize Kubernetes clusters.
Kubernetes Explorer also provides the ability to conduct a retrospective analysis of ephemeral container environments, an ability to visually map how workloads are distributed across Kubernetes clusters, and full visibility of YAML file configurations.
In general, IT organizations are entering an era where the expectation is they will have access to AI tools that make it possible to manage increasingly complex IT environments, said Burton. The simple fact is that the complexity of these environments has reached a level that makes it all but impossible to manage them without the aid of AI, he noted.
The AI Agents developed by Observe are orchestrated by a master “AI Planner” that manages the troubleshooting workflow. In effect, DevOps and IT teams will find themselves collaboratively working alongside digital assistants that are trained to perform tasks that previously would have required a significant amount of manual effort to complete.
The only way organizations are going to be able to embrace platform engineering as a methodology for managing IT environments at scale using DevOps practices will be to employ AI agents to automate tasks, noted Burton.
It’s still early days so far as applying AI agents to IT operations is concerned, but arguably the only way to get the most return on any investment in an observability platform is to rely more on AI to surface actionable insights. Being able to collect all the metrics, logs and traces in the world doesn’t mean much if IT teams can’t make any sense of the data.
The one certain thing is DevOps teams that are usually responsible for managing Kubernetes are leading the charge when it comes to both observability and AI. A Techstrong Research survey finds 63% working for organizations that will be making additional investments in observability over the next two years, with 21% describing those investments as significant. Nearly half (48%) work for organizations that already practice observability regularly.
Similarly, the survey finds a third (33%) are working for organizations that make use of AI to help build software, while another 42% are considering it.
One way or another, the way IT is managed is about to dramatically change in the age of AI. The only thing left to determine is how quickly and to what extent.
