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Grafana Assistant in Grafana Cloud Introduced

Grafana Labs launched Grafana Assistant in Grafana Cloud in private preview.

Grafana Assistant in Grafana Cloud is a tightly integrated context-aware chat experience. It connects users to their observability data through a flexible interface that lets them ask anything, go places, make changes, and even run investigations in natural language.

Users new to the Grafana ecosystem can learn more about general concepts just by asking, and as they dig into specifics, the agent will drill into actual observability data available via Grafana to provide highly contextual answers to questions. More experienced users can run queries in natural language and even have data analyzed as part of a multi-step investigation.

Grafana Assistant appears as a sidebar within the Grafana interface, receiving context about the current page and providing relevant suggestions. Use cases for Grafana Assistant are limitless, but the team concentrated on a few core areas to start, ensuring it’s easy to interact with the agent through natural language to:

  • Ask questions about their observability data.
  • Navigate to specific views for metrics, logs, traces, or profiles.
  • Make bulk changes to dashboards.
  • Create new dashboards through natural language descriptions.
  • Perform multi-step investigations by following leads in their data.

"As the world's most ubiquitous visualization platform, Grafana is evolving to incorporate the latest technologies that are transforming our industry. With Grafana Assistant, we're making AI-powered observability a reality, not just as a concept but as a practical tool that helps users more quickly and easily diagnose issues, respond to incidents, build dashboards and alerts, and more – regardless of where their telemetry lives or how it's structured," said Tom Wilkie, CTO, Grafana Labs. “Grafana’s open source roots provide a unique advantage for our AI assistant; the wealth of content on the open web produced by our global community has enabled foundation models to be experts on Grafana, Prometheus, and Loki out-of-the-box. Our LLM-based agent was built to hit the ground running and provide meaningful assistance from day one.” 

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Grafana Assistant in Grafana Cloud Introduced

Grafana Labs launched Grafana Assistant in Grafana Cloud in private preview.

Grafana Assistant in Grafana Cloud is a tightly integrated context-aware chat experience. It connects users to their observability data through a flexible interface that lets them ask anything, go places, make changes, and even run investigations in natural language.

Users new to the Grafana ecosystem can learn more about general concepts just by asking, and as they dig into specifics, the agent will drill into actual observability data available via Grafana to provide highly contextual answers to questions. More experienced users can run queries in natural language and even have data analyzed as part of a multi-step investigation.

Grafana Assistant appears as a sidebar within the Grafana interface, receiving context about the current page and providing relevant suggestions. Use cases for Grafana Assistant are limitless, but the team concentrated on a few core areas to start, ensuring it’s easy to interact with the agent through natural language to:

  • Ask questions about their observability data.
  • Navigate to specific views for metrics, logs, traces, or profiles.
  • Make bulk changes to dashboards.
  • Create new dashboards through natural language descriptions.
  • Perform multi-step investigations by following leads in their data.

"As the world's most ubiquitous visualization platform, Grafana is evolving to incorporate the latest technologies that are transforming our industry. With Grafana Assistant, we're making AI-powered observability a reality, not just as a concept but as a practical tool that helps users more quickly and easily diagnose issues, respond to incidents, build dashboards and alerts, and more – regardless of where their telemetry lives or how it's structured," said Tom Wilkie, CTO, Grafana Labs. “Grafana’s open source roots provide a unique advantage for our AI assistant; the wealth of content on the open web produced by our global community has enabled foundation models to be experts on Grafana, Prometheus, and Loki out-of-the-box. Our LLM-based agent was built to hit the ground running and provide meaningful assistance from day one.” 

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Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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