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By Max Johansson, Vehicular Systems, Linköping University An AUV shown for illustration. Photo by Swedish Maritime Robotics Centre (SMaRC). At KTH, the Swedish Maritime Robotics Centre (SMaRC) is continuously developing an autonomous underwater vehicle (AUV) as a research platform for studying underwater environments. The AUV has traditionally been powered by batteries, but adding a fuel cell can extend its operating range.

TL;DR “AI observability” now describes two different jobs. One is watching the LLM apps and AI agents your team ships: prompts, tool calls, token spend, and output quality. The other is putting AI agents to work on your production telemetry so incidents are already being investigated by the time an engineer logs in. Most engineering orgs in 2026 need both, and very few tools cover the two well.

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.

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.

When you’re on the go, your phone is often the only observability tool you have. And what you can do with it in the first few minutes matters. With the incredible innovation that has gone into mobile CPUs, GPUs and displays, a phone is a powerful device where you can immediately see what’s happening and start acting on it. And the Grafana mobile app now allows you to both see and act.