Operations | Monitoring | ITSM | DevOps | Cloud

The original case for multicloud still holds — but the conditions around it have shifted significantly. Here's what you should be asking before your next cloud decision. Most organizations didn’t set out to have a multicloud strategy. They just accumulated one – a mix of providers, tools, and integrations built up over time as individual workload decisions compounded into something that now requires a strategy of its own.

A new report from LeadDev and Harness makes one thing clear: AI coding tools have fundamentally changed how much code organizations are producing. What has not changed nearly fast enough is how that code gets released. The State of AI-Driven Software Releases 2026 report, based on responses from 500424 engineers across industries and company sizes, puts real numbers behind a problem that engineering leaders have been feeling for a while. AI is accelerating the code creation side of the SDLC.

The open-source landscape has witnessed another highly automated, ecosystem-level subversion. On June 17, 2026, a critical software supply chain attack struck the Mastra AI framework - a popular open-source TypeScript ecosystem used widely to build AI agents, workflows and RAG pipelines. By exploiting a compromised contributor account, threat actors successfully mass-published 144 malicious packages under the official @mastra npm scope.

In the fast-paced world of modern software delivery, compliance is often a bottleneck. While our existing OPA-based Policy as Code feature has long empowered teams to encode complex authorization checks and enforce granular governance across their DevOps workflows, we know that starting from a blank page can be daunting. Security and governance teams struggle to keep up with the volume of releases, while developers often find the initial setup of these policies to be time-consuming.

Automation has always promised the same basic things: less work, faster resolution, and more consistency. What has changed is how much judgment automation can now apply before work begins. Traditional process automation is excellent at executing known steps. Agentic automation, though, adds a new layer: AI systems that can decide which action or workflow should happen next. That difference matters a whole lot.

Idle resources in your data center are a liability. Every unused server drawing power, every underutilized rack consuming cooling capacity, and every stranded port sitting dark on a switch represents money spent with no operational return. That's always been true for traditional compute. It's even more consequential as organizations invest in GPU infrastructure for AI workloads, where the hardware is an order of magnitude more expensive and the cost of underutilization scales accordingly.

The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value. In line with this shift, organisations are looking beyond where AI is trained to the specifics of where it is actually used.