Operations | Monitoring | ITSM | DevOps | Cloud

As Agentic AI moves into enterprise environments, IT and security leaders face a critical challenge on how to leverage advanced LLMs without exposing sensitive data, intellectual property, or proprietary configurations to the cloud. You cannot build a self-driving, autonomous IT infrastructure if your security team blocks the deployment, and that’s exactly why the Fabrix.ai platform features an Enterprise-Grade LLM Integration architecture anchored by our built-in Data Security layer.

2025 was the year that RISC-V readiness gave way to RISC-V adoption. It’s been quite a journey. What began years ago as early architectural exploration and enablement has matured into real silicon, systems, and deployments. In particular, RVA23 provides a stable and predictable baseline we can align on with our wider ecosystem of partners. At Canonical, we’re committed to making RISC-V a viable option for anyone who wishes to adopt it.

Can you create an audit trail for what your AI agent actually did, and enforce rules about what it was allowed to do? Here’s what I found after spending a session wiring the two tools together.

The Netdata Cloud MCP Server is now available — giving AI agents and assistants direct access to your Netdata through a single endpoint at app.netdata.cloud/api/v1/mcp.

A step-by-step look at how Edwin AI uses native LogicMonitor logs, topology, and context to turn root cause analysis from alert-driven inference into evidence-backed investigation. Most root cause analysis today starts with alerts and ends with explanations that sound reasonable but can’t be verified. An alert is fed into a language model, and the output looks like an answer. It often isn’t.

Sometimes the biggest shifts in technology aren’t about collecting more data — they’re about who (or what) gets to act on it. In this episode of “Grafana’s Big Tent” podcast, host Tom Wilkie, Grafana Labs CTO, is joined by Spiros Xanthos, Founder & CEO of Resolve AI, Manoj Acharya, VP of Engineering for Observability at Grafana Labs, and Cyril Tovena, Principal Engineer on the Grafana Assistant team, to discuss agentic AI in observability.

Chatbots are starting to look fairly promising for businesses of all kinds. Customers today are keen to get things resolved faster than ever. Every startup out there is tempted to take the deal. But before jumping onto the bandwagon, you need to do some thinking as to what type of chatbot you must invest in. The decisive question being, which model of conversational AI perfectly aligns with the needs of your organization.

Artificial intelligence has been a defining theme in IT operations for nearly a decade. Early AIOps initiatives focused on predictive analytics and anomaly detection, promising to reduce operational overhead and improve system reliability. While these capabilities delivered incremental value, they often fell short of transforming how operations actually functioned.