Operations | Monitoring | ITSM | DevOps | Cloud

Datadog’s 2026 State of AI Engineering report showed organizations’ LLM inputs swelling rapidly as context engineering expands. In March 2026, 69% of all input tokens in Datadog customer traces were for system prompts: internal instructions, policy definitions, and tool guidance providing context and guardrails around the user input. This suggests that most context engineering spend among Datadog customers is going toward optimizing repeating system prompts in heavily scaffolded agent systems.

TL;DR Katja Rantala thought her dream job involved international relations and diplomacy. Turns out leading people and companies through change is what she was meant to do. “Some days everything is on fire and it's a catastrophe. The important question is whether the big picture moved.” That is Katja Rantala's description of what it is like to work in an environment that keeps changing shape.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.

Summary This guide walks through an end-to-end Grafana and InfluxDB 3 integration using a realistic dataset you generate yourself. The tutorial covers getting data in, transforming it, connecting Grafana, and building real dashboards. Table of Contents InfluxDB and Grafana are the most common pairing in time series monitoring, and division of labor between them is simple.

We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.

If your team works with SAP HANA, the main challenge usually isn’t finding another dashboard. The real difficulty is identifying which tool can quickly help you trace vague complaints about slow transactions to their root cause. In many setups, SAP HANA monitoring is divided among native SAP interfaces, cloud monitoring, infrastructure dashboards, and the broader monitoring systems used by the rest of IT. This fragmented approach can slow down root cause analysis.

Table of Contents For most of Kubernetes’ history, memory management has been a blunt instrument. Cross your limit, and the kernel kills your container. There has been no equivalent to CPU throttling, no graceful backpressure, just a hard stop. With Kubernetes 1.37, that changes: Memory QoS, built on cgroups v2, graduates to Beta and is enabled by default.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how Scoped OAuth for Public Apps, now in Early Access, builds towards this vision. Your security team asks a simple question during a routine review: which third-party apps can reach our PagerDuty data right now, and what exactly can they do with it?

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how PD Automation Runner, now in Early Access, builds towards this vision. Your on-call engineer gets paged for a critical alert on a self-hosted Kubernetes cluster. If this were a cloud-hosted service, Workflow Actions and the SRE Agent would already be working the problem: pulling logs, checking health, surfacing a fix.