Operations | Monitoring | ITSM | DevOps | Cloud

AI Observability in Grafana Cloud: A complete solution for monitoring your agentic workloads

The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem.

Introducing o11y-bench: an open benchmark for AI agents running observability workflows

Evaluating agents is hard. Verifying observability tasks is harder. Yes, AI agents have gotten dramatically and quantifiably better at coding and tool use, but observability presents a different kind of challenge. In a real incident, the hard part is rarely just writing a query. It's deciding which signal matters, figuring out whether a spike is noise or symptom, correlating metrics with logs and traces, and sometimes making a change in Grafana without breaking the dashboard another engineer depends on.

Claude Opus 4.7 Pricing In 2026: What It Actually Costs (And Whether It's Worth It)

Claude Opus 4.7 holds at $5/$25 per million tokens — but a new tokenizer inflates costs up to 35% on identical text. Here's what Opus 4.7 actually costs at production scale, how it compares to Sonnet 4.6, and the six levers that determine where your bill lands.

Building for the Agentic Era: Engineering Excellence at Harness | Harness Blog

As AI agents become ubiquitous across the software development lifecycle, engineering teams must do more than adopt new tools; they must redesign how they build, verify, and operate software. This post distills the vision, priorities, and best practices that guide engineering excellence at Harness. Different products sit at the heart of the Harness platform.

Before You Deploy Another Agent, Read This

Enterprise boardrooms are not debating whether to adopt agentic AI anymore. The debate has moved to a harder question: why do so many agentic deployments stall between pilot and production? ServiceNow's Enterprise AI Maturity Index 2026 puts a number to it. Most enterprises that have invested in AI tooling report that their biggest obstacle is not model quality or compute cost. It is the infrastructure that those agents are expected to operate within. The models are capable.

What's New in VictoriaMetrics Cloud Q1 2026? Logs, MCP Server, Better Alerting, and... a Secret Project

Q1 2026 has been one of our most eventful quarters yet for VictoriaMetrics Cloud. We shipped something we have been building towards for a long time, crossed a few infrastructure milestones, and started clearing the path for what is coming next to the most performant observability stack.

Grafana Assistant everywhere: Customize and connect to the AI agent to fit your specific needs

The ways you and your teams build and observe your systems are changing. It’s no longer just engineers looking at dashboards, or writing queries or config files. More often, it’s an agent interacting with the data, too, helping write code, run applications, investigate incidents, rightsize deployments, and more.

Why Mid-Market IT Teams Are Drowning in Tickets - And How AI Concierges Are Finally Fixing It

Every IT leader I've spoken to at a mid-market company (50-500 employees) tells me some version of the same story. Their team is good. Their tools - usually ServiceNow, Jira Service Management, or Freshservice - are solid. But the volume of inbound requests is relentless. Password resets at 9am. VPN issues at 2pm. "My Zoom isn't working" at the worst possible moment before a client call. The tickets never stop, and the IT team never has enough bandwidth to focus on the work that actually moves the business forward.