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.

Learn why modern IT complexity can slow incident response and how observability, AIOps, operational context, and automation can help teams detect, investigate, and resolve issues faster.

Welcome to the third and final part of our series on AI norms and values. Parts of this doc were extracted and published separately on substack; as a whole, they describe the principles we hold pertaining to technology and AI, and the ethical commitments we make to each other and our customers. We set out to write about AI, and ended up writing about ourselves. These documents are not meant to be aspirational ones; they are derived from how we do our work every day in honeycomb.

As agentic AI workflows gain traction within organizations, those organizations are asking how to account for their behavior while keeping costs manageable. Some are sticking with the old three pillars of observability approach: take a measurement to create a metric, record output to a log, and track serial progress with a trace. Each of these is useful, but treating them as distinct formats from the start means paying for them distinctly too. Separate storage doesn't come cheap.

Eight LLM and AI agent observability platforms compared: what each tracks, pricing and free tiers, self-hosting options, and who each is built for. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

I'm investigating repeated errors in my e-commerce application, and I need to get enough context in a single Honeycomb query to piece the entire picture together. Each query returns events based on the event's WHERE clauses, but I want to know several things from outside of the event that recorded an error. Things like: Those attributes are all over the trace. That's going to make a single query tough, right? Wrong!

A critical system has to stay reliable without new failures or added downtime, and live debugging, confirming the root cause without stopping the system, is often the only way to do that. In practice, this means having runtime context: on-demand evidence generated at the point of failure rather than logging configured months earlier, which is what keeps MTBF up, MTTR, and MTTA down.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.