Operations | Monitoring | ITSM | DevOps | Cloud

OpenTelemetry graduated from the CNCF in May 2026 as, in the foundation’s own words, the de facto observability standard. The JavaScript API package alone did 1.36 billion downloads in twelve months. That kind of win has a side effect nobody plans for. Once a wire format is everywhere, has a receiver for every source, a transform language, and an agent your platform team already operates, people start putting things on it that have nothing to do with knowing whether a service is healthy.

In order for users to get the most out of the data being sent to Sentry, it’s important that we make it easy to find that data. Our team works on features to help users browse their data to find a particular event using search queries and filters. The search bar enables users to find their data by specifying search terms. Searching uses the Sentry Search Syntax, which can be barrier for users.

ChatGPT Work has become a common starting point for data and product teams. Analysts open it to compare launch adoption across segments, diagnose a metric that moved overnight, or turn a week of scattered numbers into a readout that a leader can act on. But the moment teams ask whether their experiment actually caused an effect they’ve observed, the conversation stalls.

An LLM gateway is a proxy that sits between your applications and model providers, handling routing, failover, caching, and cost controls through one API. The strongest picks in 2026: LiteLLM for self-hosted control, OpenRouter for instant multi-model access, Portkey for managed governance, and Bifrost for production-scale throughput. Enterprises spent $37 billion on generative AI in 2025, a 3.2x jump in one year, per Menlo Ventures.

Engineers ignore cloud costs because of broken feedback loops, not apathy. Learn what AI cost management is, why AEO matters more than ever, and how a cost management agent embeds accountability directly into engineering workflows. Engineers ignore cloud costs because cost data arrives too late and too disconnected from their workflow to act on.

Over the summer, Fly.io founder Kurt Mackey announced a complete pivot for the company toward "Computers for Agents", which are ephemeral virtual machines (called Sprites) optimized for AI coding workflows. He was refreshingly explicit about what this means: they are not trying to do both traditional application hosting and AI agent compute. They are choosing one over the other. This is a completely rational bet on the future of developer tooling.

AI agents can help investigate failed builds, fix flaky tests and automate other development tasks. But setting up those agents has required more Pipelines configuration than it should. Agent-powered steps often need different compute, permissions and runtime settings from ordinary build and test steps. Until now, teams have either repeated those settings across every agent-powered step or tried to make one set of global defaults work for everything.