Operations | Monitoring | ITSM | DevOps | Cloud

Half of the Checkly CLI users are already coding agents. This is not a prediction — it's what the data shows today. Since February, more and more agents have been using the CLI to manage and configure their Checkly monitoring setups. Right now, we're at 50% human and 50% agentic CLI users. And we predict that by the end of 2026, it won't be humans using the CLI; the agents will have taken over. The terminal became the primary interface for AI agents doing real work in the Checkly ecosystem.

November 24th, the Opus 4.5 release turned around the entire tech industry. This was the moment when agents became capable. Capable enough to write solid staff-level code. Capable enough to reason about alerts, investigate root causes much faster than most engineers, and set up the reliability layer faster. For me, this feels like an iPhone moment on steroids; the adoption of AI is accelerating much faster than any adoption curve I’ve seen over the past few decades.

The quick download: AI reduces MTTR by helping teams detect issues sooner, pinpoint root causes faster, and resolve incidents with less manual effort. IT downtime costs organizations an average of $9,000 per minute. AI-powered observability can cut incident resolution time by up to 70%. Here’s what it takes to get there. Every minute an incident goes unresolved, the meter is running.

As organizations adopt AI-assisted development and increase their release velocity, they are not only generating more code but also finding more vulnerabilities from static analysis. The traditional remediation workflow of manually triaging issues, creating tickets, and opening individual pull requests (PRs) cannot keep pace. Fixing tens of thousands of vulnerabilities one by one is not a viable remediation strategy.

As AI-powered products and services become central to how organizations operate, the need for responsible AI governance has never been greater. Customers, partners, and regulators are seeking assurance that AI systems are built, managed, and monitored responsibly and effectively. Datadog is committed to the responsible use of AI, both in how we build our products and in how we help customers observe their AI workloads.

Nutanix is a hyperconverged infrastructure (HCI) platform that combines compute, storage, and virtualization into a single software-defined stack. By collapsing traditional infrastructure tiers into one platform, Nutanix simplifies provisioning and operations for virtualized workloads. Clusters are managed through Prism Central, which provides visibility into health, performance, capacity, and operational activity across hosts and VMs.

In December 2025, we announced our intention to introduce pricing for self-hosted runners so we could provide stronger support and keep investing in new features and ongoing improvements. You’ve told us that having a free option is important. As a result, we’re introducing a new operating model that lets you continue using self‑hosted runners for free with the option to upgrade to a paid premium runners tier as your needs grow.

Employee offboarding looks simple from the outside. Someone gives notice, HR processes the paperwork, and IT handles the rest. In practice, "the rest" is where things get complicated. A single departure can touch dozens of systems, and the handoffs between them (between HR and IT, between tools, between teams) are exactly where access stays on longer than it should, steps get missed, and audit findings show up months later.

Vector Search GA & SQL Enhancements. Aiven for ClickHouse 25.8 is now available as an Early Availability. This Long-Term Support release introduces lightweight projections as secondary indexes, general availability of vector search with binary quantization, correlated subqueries for broader SQL compatibility, lightweight updates for MergeTree tables, and significant performance and data lakehouse improvements.

Every incident arrives with a payload, and that payload usually tells you far more than whether something broke. It points to which service is affected and how serious the issue looks. It also carries context about which customers are on the receiving end of that failure. The service name, severity, customer context — all of it can feed directly into routing decisions. This guide explores how to read those parts of the payload and use them to route incidents automatically.