Operations | Monitoring | ITSM | DevOps | Cloud

In 2026, we are seeing incredible feats of engineering with agentic AI, impacting metrics and distributed traces that map thousands of microservices. Our systems have never been more intelligent and complex. However, as our observability becomes more intelligent, fewer employees know how to manage and troubleshoot complex systems. These employees, who often bear the brunt of an error’s impact, may need to rely on specialists to interpret the system.

In late 2025, we introduced Trace Highlight Comparison. This capability was designed to solve the problem of having too many spans. This causes technical and financial challenges when identifying performance patterns within high-volume telemetry streams. The goal is to avoid massive indexing costs and eliminate the ingestion latency associated with indexing every record. However, knowing these trends is only half the battle.

It’s often framed as an enterprise-only exercise: long timelines, expensive tooling, consultants everywhere, and a lot of compliance work that exists mainly to survive an audit. As a ~40-person, engineering-driven SaaS company, we needed the same level of trust and rigor as much larger organizations — but we weren’t willing to accept shelfware, parallel compliance infrastructure, or controls that only exist on paper. We also didn’t stop at ISO 27001.

Grok structures logs. Context engineering connects systems. AI explains behavior. For years, Grok patterns have been the workhorse of the SRE world. Built on regular expressions, Grok helps teams extract structure from unstructured logs. As we explored in "Do You Grok It?", Grok is the key to turning messy log lines into usable fields. It's why our Grok Pattern Reference remains one of our most-visited resources — SREs are hungry for structure.

Over the course of my 20 years as a developer, SRE, and now observability product leader, software has typically progressed at a good pace. But now, the emergence of two transformative technologies are fundamentally reshaping enterprise observability: generative AI (GenAI) and OpenTelemetry (OTel). We surveyed over 500 IT decision-makers for a new report:The Landscape of Observability in 2026: Balancing Cost and Innovation.

OpenTelemetry instrumentation is the foundation of modern microservices observability, but getting it right in production requires more than just enabling auto-instrumentation. This guide covers production-tested OpenTelemetry best practices that help engineering teams achieve reliable distributed tracing, control observability costs, and extract maximum value from their telemetry data.

This guide shows you how to implement OpenTelemetry’s auto-instrumentation for complete distributed tracing across your microservices, from initial setup through production optimization and troubleshooting.