Operations | Monitoring | ITSM | DevOps | Cloud

Walk into a network operations center this year and the useful AI is not running the place. It is doing three specific jobs, and doing them well: cutting an alert storm down to the one incident that matters, pointing at the likely cause, and deciding what deserves a human’s attention first. That is where AI in NetOps pays for itself right now. The part worth noticing is that all three jobs lean on the same thing.

For the past couple of years, most conversations about AI in business have centered on adoption. Which tools should organizations deploy? Where can AI improve productivity? How quickly can it deliver value? Those questions still matter, but they are no longer the most difficult ones to answer. AI is now appearing across business applications, employee workflows, development environments, and customer-facing services.

Organizations are increasingly using multiple models to build AI agents in order to find the best balance of performance and cost for each agentic task and LLM call. As we discovered in the 2026 State of AI Engineering report, there isn’t currently a clear winner in terms of adoption among competing models and many organizations are keeping older models in flight despite frequent new releases.

AI agents are provisioning infrastructure now, not just writing code. Qovery Blueprints let you define what an agent, or a developer, is allowed to deploy and under what cost rules, so that self-service or delegation doesn't mean losing control. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.

Most cloud-native cost tools are built to find and address waste reactively. Underutilized nodes, oversized requests, and idle workloads are revealed in the utilization data, the fixes are well documented, and the initial savings these tools drive are very real. But what we’ve seen consistently across clusters is a different category of blocker, one that quietly prevents consolidation and strands capacity your autoscaler can never reach. They don’t surface in dashboards as obvious waste.

Helsinki, Finland — Aiven has acquired Flow AI, a company building infrastructure for production-grade analytical AI agents. The integration of Flow AI technology will accelerate Aiven's product roadmap and make it easier for customers to securely and scalably run production AI applications and agents next to their data.