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The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

Location Management in Skylar One

Location Management in Skylar One gives IT teams a centralized way to create, maintain, and organize location data, building the foundation for trusted geographic visibility across distributed environments. In this walkthrough, Brian Harding, Director of Product Management at ScienceLogic, demonstrates how to create and manage locations in Skylar One. See how teams can define locations, associate them with the right organizations, and use accurate latitude and longitude data to establish the geographic context needed for devices, services, and Geographic Maps.

Geographic Maps in Skylar One

Geographic Maps in Skylar One give IT teams a faster, more intuitive way to understand infrastructure and service health across distributed environments. In this walkthrough, Brian Harding, Director of Product Management at ScienceLogic, demonstrates how to create and configure a Geographic Map in Skylar One. See how to create locations, align devices and services, use filters to control what appears on a map, and visualize infrastructure health based on real-world locations.

Why AI Agent Architecture Needs a Runtime Context Layer

Every AI agent architecture diagram shows the same five layers: perception, memory, reasoning, action, and feedback. Each layer assumes the one before it worked correctly, and none of them can confirm that once the agent runs against live production data. Runtime context is the sixth layer most designs leave out, and it’s the one that decides whether any of the other five can be trusted.

Why AURA Scratchpad Is Rad: Bound the AI SRE Agent Context Window

A big tool result does not have to be a big context cost. AURA moves it to disk and hands the model a pointer plus the tools to navigate what is there. A large MCP tool result can consume or overflow an agent's context window, and on a third-party server you do not control how much comes back. Scratchpad breaks the link between how big a tool result is and how much context it costs: the full output goes to disk, and only the slice the model asks for ever enters the window. Errors always pass through inline, so the model can react to them.

Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026

It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.

Autonomous IT and the Five Forces Reshaping IT in 2026

Autonomous IT is the focus of this LogicMonitor fireside chat with CMO Brooke Cunningham and CPO Garth Fort, built for enterprise IT leaders, IT operations, and observability and AIOps teams. Brooke and Garth break down the 2026 Observability and AI Outlook for IT Leaders report, based on a survey of 100+ VP-level IT leaders who own observability budgets across North America, EMEA, and Asia Pacific.

MCP Won't Replace Your Monitoring Tool

MCP is generating a lot of hype nowadays (but then again, almost anything that emerges in AI seems to attract hype). The anticipation around it is similar to the level of excitement that would break out if Apple were to finally introduce USB-C to iPhones. To be fair, though, some of that hype is warranted, considering the fact that MCP provides a standardized approach to connecting agents with third-party tools, which significantly simplifies this type of integration (hence the USB-C analogy).

How to add Software Catalog metadata at scale with Terraform | Datadog Tips & Tricks

Adding metadata to Software Catalog entities manually is a tedious process that doesn’t scale as your service count grows. This video shows you how to automate that work with Terraform so you can add shared metadata across existing Software Catalog entities at scale.