Operations | Monitoring | ITSM | DevOps | Cloud

AI coding agents are excellent at writing code. Ask Claude Code, Codex, or Cursor to add a feature, and it just works. At Checkly, we were ready for the new agentic world from the start! Monitoring as Code means your entire monitoring setup lives in your repository. API Checks, Browser Checks, alert channels, status pages; everything is defined in code, managed with the Checkly CLI, and version-controlled like any other part of your stack.

SREs and application developers rely on telemetry data to understand and improve their systems. As organizations scale and evolve, those systems generate an ever-growing volume of metrics, logs, and traces. But more data alone does not make it easier to improve performance or reliability: Identifying meaningful optimizations still requires careful investigation and analysis.

Last time, we explored the concept of variable scopes in Icinga 2, which help you manage and organize your DSL configurations effectively. As promised, today we’ll dive into another, how shall I say, advanced topic: Namespaces in Icinga 2.

In the first two posts of this series, we explored how alert noise emerges from design decisions, and why notification lists fail to create accountability when responsibility is unclear. There’s a deeper issue underneath both of those problems. Many alerting systems are designed without being clear about the outcome they’re meant to produce. When teams don’t explicitly decide what they want to happen as a result of a signal, they default to the loudest option available.

Starting from today, Healthchecks.io users on all plans (Hobbyist, Supporter, Business, Business Plus) can invite an unlimited number of users into their projects. Previously, the limits were: 3 team members for Hobbyist and Supporter, 10 team members for Business, and unlimited team members for Business Plus. From now on, it is unlimited for all.

Hybrid and multi-cloud monitoring breaks down when teams can’t connect signals to customer impact fast enough to act. Hybrid and multi-cloud sound simple: run some workloads in public cloud, keep some on-premises, and connect it all. But in practice, you’re managing dependencies across teams and systems, tools that don’t share context, and incidents that refuse to stay in one place.

In a global microservices architecture, technical performance initially presents as a chaotic stream of disconnected telemetry. For a Technical Program Manager (TPM), success depends on the ability to move past these disconnected individual data points to identify stable patterns. If they have services entering critical states, looking at individual logs or traces is inefficient. Protecting system reliability requires an engine that can automate pattern recognition at scale.

We’ve made another improvement to the StatusGator API based directly on customer feedback. Many teams are managing monitors programmatically, and one recurring request was the ability to fully manage monitor cleanup via API. You can now delete all monitor types through the API.