Operations | Monitoring | ITSM | DevOps | Cloud

An AI agent doesn’t just answer questions—it takes actions in real systems. As teams across the enterprise build their own agents, governance can’t be an afterthought. Learn how agents work, why they create new risks, and what controls are needed before they act, not after.

Organizations are spending more on GPUs as they move AI workloads into production, and improving GPU utilization is a key priority. But Kubernetes’s legacy device plugin model can limit those improvements because it constrains how workloads request and consume GPU resources. Teams must configure GPU partitioning before workloads are scheduled. Resource requests cannot distinguish GPUs by capability, and a workload can reserve an entire GPU while using only part of its capacity.

Frontend and mobile applications depend on technical steps that users expect to work in every user journey: loading search results, signing in, saving a payment method, or processing a payment. Datadog Real User Monitoring (RUM) captures detailed events about these interactions, including views, actions, resources, and errors. But examining those events individually does not always provide a direct answer to more fundamental questions: Did a critical application step work, and how long did it take?

Today, we’re introducing Scout 1.0, Datadog’s first AI model specialized for investigating production issues. Scout powers Bits Investigation, our AI SRE. We trained Scout to reduce inference cost so that investigations are cheap enough to run an order of magnitude more often and earlier in an incident’s lifecycle. Key highlights include: Close dialog.

Every age has had its spark, the moment an idea stops being an idea and becomes something a whole country can build on. Steam, electricity and the web all began with people who could see what was possible, but each one only changed the world once someone built the infrastructure to make it real. Britain has never been short of those people. Our universities produce world-leading AI research, our engineers are among the best anywhere, and our founders build companies the rest of the world wants to buy.

You already know the effectiveness of the JFrog Platform – blocking risky packages, speeding up builds, securing the supply chain, providing end-to-end governance and so much more. But when it comes time to renew the license, a new VP may ask: “What are we actually getting in return?” or finance wants to understand the ROI: can it really be proven by the numbers?

Today Canonical announced the release of Ubuntu 26.10, codenamed “Stonking Stingray,” available to download and install from ubuntu.com/download. Building on previous releases, Ubuntu 26.10 advances enterprise security features, completes the rollout of the Rust-based uutils coreutils, and strengthens integrations for user authentication using Microsoft Entra ID.

At home I run fully automated coding loops on a few side projects. They use more than 100-million tokens a day. At Claude Opus 5.5 prices, that costs more than $5,300 a month. For hobby projects. The same usage on DeepSeek V4.1 Flash cost about $330 a month, one sixteenth of the price. If we want to keep scaling how much we use AI, most of those tokens have to run on open models, period.

Adaptive Metrics , which is part of the Adaptive Telemetry suite, looks at how your metrics are actually used and recommends how much detail you should keep for each metric. This helps you cut costs while still retaining the data you actually need. Last year we added auto-apply capabilities that let you implement those recommendations automatically, but only with coverage-first behavior, maximizing coverage of whatever you're querying.