Operations | Monitoring | ITSM | DevOps | Cloud

We’ve all had that moment where it seems like you've solved your company's biggest engineering challenges after a weekend of hacking something together. Your prototype is so good, you feel, that the obvious next steps are to build a slide deck, rally the team around your work, and prepare the ticker tape parade for your hero's welcome. Jeff Schnitter, a Solution Architect at Cortex, knows this roller coaster of experience all too well after his time at Workday.

With modern tooling and agentic coding assistants, straightforward bugs are almost a relief. If a test can catch it, or a user can reproduce it, chances are you can squash it quickly. The harder category — and the one worth writing about — are the bugs where everything looks correct. Your code runs, no exceptions are thrown, your debug statements confirm the right functions fire at the right times, and yet nothing works.

Healthcare is shifting fast, and artificial intelligence is no longer a future concept sitting in research labs or pilot programs. It’s already embedded in clinical workflows, operational systems, and patient interactions, often in ways that feel subtle, uneven, and sometimes uncomfortable.

For many software teams, documentation is written after features are built and design decisions have already been made. When that happens, questions about how a feature is understood or used often don’t surface until much later. A little over one year ago, our team began to recognize this pattern in our own work. Features generally functioned as intended but were difficult to use or explain. Documentation lagged behind releases.

Explore how AI-driven automation is reshaping IT service management for a faster and smarter enterprise in 2026.

The Iron Man’s J.A.R.V.I.S. is the artificial intelligence (AI) that almost every person wants to see. A conversational technology that answers questions like a friend would. The rise of large language models (LLMs) almost seems to give people the friendly robotic sidekick that generations of children grew up dreaming about.

When you ship a Next.js app, the React and TypeScript you write aren’t what your users actually download. Next.js compiles, minifies, splits, and shuffles your code into chunks in ways that are great for performance and terrible for debugging. This post shows you how that pipeline works, how source maps and debug IDs connect it all back to your original code, and how to wire things up so Sentry shows you real file names and line numbers instead of an unreadable stack trace.

Modern IT operations sit at the center of revenue, customer experience, and business continuity. Every decision engineers make influences far more than the technical domain, which is why teams need intelligence they can validate, reasoning they can understand, and guidance they can rely on. In an environment shaped by rapid change and expanding dependencies, decisions must be grounded in accuracy and context to avoid unnecessary risk.

At the beginning of February, we released v3.1 of Refinery, our advanced, tail-based sampling solution. The new version comes with more performance enhancements, bug fixes, and a few new pieces of telemetry. In tandem with the 3.1 release, we also released a new tool for our MCP server which helps your AIs understand Refinery, and how Honeycomb handles sampling.

At Grafana Labs, we are fully committed to the open source OpenTelemetry project and are actively engaged with the OTel community. Many Grafanistas spend a large proportion of their time contributing directly to OpenTelemetry upstream projects, helping make observability more powerful, reliable, and accessible for everyone as part of our big tent philosophy.