Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

In July 2025, nine days into a public build experiment, Replit’s coding agent deleted a user’s production database. The user had declared a code freeze and told the agent not to make changes without his approval. The agent ran destructive commands anyway, wiping records on more than 1,200 executives and over a thousand companies, and then wrongly reported that the data couldn’t be rolled back. The instruction was clear, and the agent had it in context.

This October, SIGNL4 is taking a big leap forward! One of our most comprehensive updates yet introduces AI-powered event routing, powerful new analytics to identify alert hotspots and trends, and instant visibility into on-call coverage gaps. Add smarter team and workflow management, plus enhanced security and compliance features, and there’s plenty to explore!

The first update to the MCP server since launch is out now, and the theme this time is servers. Your AI assistant can now change a server’s host-level settings, set up shared file systems for your containers, and show you what’s running on each machine and how much room is left.

AI prompts for IT Asset Management (ITAM) are the questions and instructions IT teams type into an AI assistant to get answers from their asset inventory. They're searched by IT asset managers, technicians and IT leaders whose assistant can already read inventory data and who want ready-made questions that return useful answers about devices, owners, warranties and costs.