Operations | Monitoring | ITSM | DevOps | Cloud

I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.

The rapid pace of modern software development, fueled by AI-driven coding and accelerated deployment cycles, has resurfaced a challenge that many development teams already struggled with: the speed of incident response must now match the speed of change. Every day, teams ship code faster than ever, which inevitably increases the risk of a new issue making it to production. The traditional approach—where engineers waste time jumping between disconnected tools—is no longer sustainable.

Innovation is moving faster than ever, but software delivery has become the ultimate chokepoint. While AI coding assistants have flooded our repositories with an unprecedented volume of code, the teams responsible for actually delivering that code, our Platform and DevOps engineers, are often left drowning in manual toil. If you’re managing Argo CD at an enterprise scale, you’re painfully familiar with the "Day 2" reality.

‍Why 90% of AI prototypes never make it to production, and what to do about it. Every week, someone on my team shows me a demo that looks incredible. An agent that writes deployment pipelines. A chatbot that triages incidents. A copilot that generates test cases from Jira tickets. The demo takes 20 minutes. The audience claps. Everyone leaves convinced we're six weeks from shipping it. We're not.

The previous two posts in this series have looked at some of the use cases Honeycomb customers are implementing to observe LLMs in production and power agentic observability workflows. In this third and final post, we’ll take it back to basics and look at how the fundamental capabilities and infrastructure of Honeycomb provide the comprehensive data and fast performance that makes these use cases work at production scale. AI capabilities built on a weak observability foundation fall apart fast.

In our previous post in our series on observability for the agent era, we looked at how Honeycomb provides unique visibility into LLMs operating in your production environment. Now, let’s flip it around and explore how Honeycomb provides observability insights uniquely suited to helping your AI agents rapidly diagnose and fix production issues, and build production feedback into the next round of development.

The agent era is here. Engineering teams are shipping AI-powered products, deploying multi-agent systems, and trying to figure out what observability even means for non-deterministic systems.