In addition, the company is previewing an ability to apply artificial intelligence (AI) to incident management to make it simpler to surface the root cause of an issue. Sift is a diagnostic assistant in Grafana Cloud that automatically analyzes metrics, logs and tracing data, while Grafana Incident is a generative AI tool that summarizes incident timelines with a single click, creates metadata for dashboards and simplifies the writing of PromQL queries.
Grafana Labs is also making generally available an Application Observability module for Grafana Cloud to provide a more holistic view of IT environments.
Finally, Grafana Beyla, an open source auto-instrumentation project that makes use of extended Berkeley Packet Filtering (eBPF), is now also generally available. That tool enables DevOps teams to collect telemetry data for an IT environment from a sandbox environment running in the microkernel of an operating system. That approach makes it simpler to automatically instrument an IT environment, but there are instances where DevOps teams will be managing complex applications that will still require them to collect telemetry data via the user space of an application.
Richi Hartmann, director of community for Grafana Labs, said collectively, these additional capabilities will make it simpler to apply observability across increasingly complex IT environments. For example, the AI technologies developed by Assert.ai will make it possible for DevOps teams to start sending data to Grafana Labs that will enable the cloud service to identify the applications and infrastructure being used. AI models will then be able to automatically generate a custom dashboard for that environment that DevOps teams can extend as they see fit, said Hartmann.
In general, machine learning algorithms and generative AI are starting to be more widely applied to observability. The ultimate goal is to automatically identify issues in ways that reduce the cognitive load required to manage complex IT environments while also making it easier to launch queries that identify bottlenecks that could adversely impact application performance and availability.
It’s not clear to what degree observability tools might eliminate the need for monitoring tools that track pre-defined metrics, but most DevOps teams will likely be using a mix of both for the foreseeable future.
In the meantime, IT environments are only becoming more complex as various types of cloud-native applications are deployed alongside existing monolithic applications that are continuously being updated. The challenge is the overall size of DevOps teams is not expanding, so there is a greater need for tools to streamline the management of DevOps workflows.
AI will naturally play a larger role in enabling organizations to achieve that goal, but it’s not likely to replace the need for DevOps engineers, said Hartmann.
Conversely, many DevOps teams will also naturally gravitate toward organizations that make the tools they need to succeed available. Today, far too many manual tasks are increasing turnover as DevOps teams burn out. Organizations that want to hire and retain the best DevOps engineers will need to invest in AI.
Of course, DevOps, at its core, has always been about ruthlessly automating as many manual tasks as possible. AI is only the latest in a series of advances that, over time, continue to make DevOps more accessible to IT professionals of all skill levels.