Operations | Monitoring | ITSM | DevOps | Cloud

Yesterday, my partner told me about his new automated pipeline from a conversation recording to publication of a whole podcast, with social media blurbs and graphics and everything. It did more than he'd predicted the agent could do. "We are limited by what we can imagine," he said. Cheesy, but it kinda gave me goosebumps because it's (almost) true. Most of the experienced developers I know are concerned.

We just wrapped O11yDay London, and one theme ran through every session: writing code is no longer the hard part. Review queues, attention, operating agents in production, runaway token bills, and proving any of it is worth the money are where teams are getting stuck. The teams making progress are the ones who can see what's happening in production. Here are some highlights. We'll have videos available in the near future.

We're excited to share that Honeycomb has achieved yet another Amazon Web Services (AWS) Competency—the Resilience Technology Competency—which recognizes our ability to help customers detect, investigate, and recover from failures in production systems running on AWS. While receiving the competency designation is a fantastic recognition of the capabilities of the Honeycomb platform (we also received one in Financial Services earlier this year), it's ultimately not the point.

Your LLM application emits telemetry unlike anything else in your stack. Model calls, tool invocations, retrieval steps, and token usage arrive as spans whose attributes carry entire prompts and completions. That data is bulky, it's full of user content you may not be allowed to export, and depending on which instrumentation each service uses, the same fact can arrive under different attribute names.

Back in July, I wrote about how the Tenant team (the team behind Honeycomb Private Cloud (HPC)) has embraced the code review bottleneck to focus more of its work. One of the other challenges we have is that we're downstream of almost all the other teams at Honeycomb, meaning that we have to package up everyone's code and services, and how it gets provisioned! This is something impossible to handle through code review since there are so many engineers on other teams, and so few of us.

What happens when a software engineer who has put their entire identity into being the resident expert of an obscure technology or language with ten years of experience and who knows the codebase like the back of their hand now has to compete with AI? Psychologically speaking, according to Dr. Cat Hicks, author of The Psychology of Software Teams and founder of Catharsis, that's called identity threat, and it's a surefire way to feel unsafe. We were honored Dr.

Earlier this year, we introduced more capabilities to support agents in production, a more chaotic, complex environment that requires a tremendous amount of context to understand. Unlike tools that capture shallow, pre-aggregated metrics, or cannot join a metric, trace, and log in one query, Honeycomb retains the context and connective tissue from telemetry data to build a nuanced view of production.

A few months ago, we launched Agent Timeline to close the gap between knowing an agent failed and understanding why. It took the tangled reality of a multi-agent, multi-trace workflow and rendered it as a single, readable conversation: every LLM call, tool invocation, handoff, and downstream system span laid out in the order they happened.