Operations | Monitoring | ITSM | DevOps | Cloud

Automatically starting investigations from an alert with Assistant Investigations

Staff Software Engineer Alexander Sniffin demonstrates how you can use Assistant Investigations to automatically start an investigation for you when an alert fires in Grafana. When you receive an alert, Investigations can do the work to figure out the root cause so that you don't have to spend your time doing so. Assistant Investigations is now generally available for Grafana Cloud.

Only 1 in 4 Employees Follow AI Policy: How to Fix It

AI is more than just a tool—it's a transformative experience for the modern workforce. But as employees "run fast" to adopt AI, a critical gap is forming between innovation and safety. In this video, Brooke Johnson, Ivanti’s Chief Legal Counsel and SVP of People and Security, discusses the "natural tension" between AI excitement and the necessity for responsible, secure implementation. With only 25% of employees reporting consistent policy adherence, the risk of "Shadow AI"—unsupervised tool use—has never been higher.

AI didn't take humans off the platform, it just changed the job they do there.

Agents are writing more of the code these days, but that doesn't make them the only user of your platform. Abby Bangser, Principal Engineer at Syntasso and CNCF Ambassador, makes the distinction: the agent might be your primary coder, while humans are still validating what it builds and interacting with the system it runs on. From a Braintrust conversation with engineering leaders on AI agents and engineering operations.

GitLens 18 Turns the Commit Graph Into an Agent Command Center

Five coding agents sounds like leverage right up until a developer is the one keeping track of all five: one fixing a bug, one building a feature, one refactoring, and two waiting on input at the same time. AI did not create that problem. It exposed a workflow problem that was always going to surface once parallel development became normal instead of occasional.

Graylog MCP Howto Webinar

In this video, we walk through connecting the Graylog MCP Server (introduced in Graylog v7.0) to Claude CLI, enabling natural language interaction with your Graylog instance through Claude Desktop. Topics covered: Whether you're a Graylog admin looking to speed up investigations or a security engineer curious about AI-assisted log analysis, this walkthrough gives you everything you need to get MCP running end to end.

Automate all the things: How to use Grafana Cloud's AI to relieve the operational burden

Continuous integration and continuous delivery (CI/CD) have dramatically changed how we ship software. But once code reaches production, the operational work is still surprisingly manual. Engineers continually monitor systems, investigate unexpected behavior, and decide which issues require action. And that is where the next opportunity for AI-driven automation lies. For example, in today's CI/CD workflows, someone refreshes the pipeline page to see whether the queue has moved.

A default is not a decision: cut AI model costs with CloudZero's free, open-source Model Rightsizer

Only 22% of finance leaders can tie their AI spend to a business outcome, according to CloudZero’s 2026 finance survey. When AI ROI falls short, it usually isn’t because a company is doing too much AI. It’s that no one is watching which model runs which task, and that one choice accounts for a large part of the cost. Here’s why it happens.

Cloud Asked What It Cost, AI Is Asking What It's Worth | Harness Blog

AI has quickly become one of the largest and fastest-growing enterprise expenses, exposing many of the same governance and visibility challenges organizations previously faced with cloud. Based on findings from the 2026 State of AI in FinOps report, we explore how mature organizations improve AI cost ownership, reduce waste, and build a culture focused on measurable business value.

Model Rightsizer: the agent that stops your other agents from defaulting to Fable

Model Rightsizer is an open-source Claude Code sub-agent from CloudZero that scores each task on capability need versus cost pressure, then routes it to the smallest model that can handle it. In its first week, it cut Opus spend 75% while shifting 234x more work to Sonnet. Every Claude Code agent you run has to answer a question it usually never gets asked: does this task need the smartest model available, or are you paying Fable prices to rename a variable across three files?