Operations | Monitoring | ITSM | DevOps | Cloud

AI has rapidly evolved from an experimental technology into a foundational capability for modern enterprises. Today, organizations are no longer asking whether AI should be adopted but how quickly it can deliver measurable operational value.

Bitbucket Pipelines has always been an engine for automating more than just CI/CD, but today, Pipelines takes a first step towards a full agentic automation platform for all the manual, tedious, repetitive work that happens before and after code creation. You’ve probably seen the stat: Development teams spend 84% of their day doing things other than building features. A lot of this work is: This work matters, but it’s not very fun.

Over the last three months, we’ve been exploring what about software development and observability changes with AI, and what doesn’t. Our conclusion: these five principles will still remain true, even when 90% of the code is AI-driven. The agentic AI space is moving fast. Models are improving, context windows are expanding, and the ways people build and operate agents are changing so fast that any thoughts we share could feel dated by the time you read this.

Alerts are meant to help teams respond quickly to problems, but too often they arrive without enough context to be immediately useful. An alert that says “CPU usage is high” still leaves the on-call engineer asking critical follow-up questions: Which service? Which environment? Where do I look next? Validating the alert and triaging the situation is the first step for every engineer. It's a manual step that takes time, extending every potential incident.

One of the most common requests we’ve gotten since launching custom dashboards is deceptively simple: “How do I put this on a TV?” Teams want their dashboards on wall-mounted screens in NOCs, war rooms, and open office spaces. The dashboard is already built. The data is already there. They just need a way to display it on a screen that nobody is logged into, without exposing the full Netdata Cloud interface. TV mode does exactly this.

In this latest ecosystem update, we’re introducing further enhancements to the Console Connect platform, with new data centre deployments and expanded availability of cloud on-ramps across key global locations.

Agentic Kubernetes resource reclamation is the practice of using an autonomous control plane to continuously identify, suspend, and delete idle infrastructure across a multi-cloud Kubernetes fleet. It replaces manual cleanup and reactive autoscaling with intent-based policies that act on business state, eliminating the configuration drift and cloud waste typical of unmanaged fleets.

If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world.

Shopping for a service desk automation platform feels like it should be straightforward. It isn't, and the reason is that the language vendors use masks how differently these platforms actually behave once they're live. Every platform claims that they automate more, resolve faster, and reduce ticket volume. That’s a given.