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”.

In March 2026, IsDown users collectively saved 10.5 hours by receiving outage alerts before vendors officially acknowledged problems. The most significant early detection gave users a 2.3-hour head start when The Federal Reserve's FedACH system experienced issues. This data reveals the persistent gap between when users experience problems and when vendors update their status pages.

DNS Check now supports two features for Enterprise accounts that make it easier to work as a team: Team Members and Additional Email Recipients. Team Members lets multiple people log in and work with your DNS records using their own credentials. Additional Email Recipients sends notification emails to people who need to stay informed but don't need to log in.

In everyday life, ignoring what you cannot see may feel harmless. In IT, it creates a false sense of security and a costly illusion. Although many organizations use some form of asset discovery, 2026 security research from Ivanti reveals that more than 1 in 3 IT professionals (38%) report having insufficient data about devices accessing their networks, and 45% say they lack adequate information about shadow IT. This lack of visibility leaves critical assets at risk of going undetected and unmanaged.

The word “Trillion” never fails to set the tech world on fire. Foundation Capital’s Jaya Gupta and Ashu Garg are two of the most recent firestarters. Late in December, they co-wrote “AI’s trillion-dollar opportunity: Context graphs,” outlining how AI will transition from organizational knowledge to organizational comprehension.

Quick answer: A cloud cost optimization framework is a structured, repeatable system for managing cloud spend across people, processes, and tools. It defines how teams gain cost visibility, allocate spend to the right owners, optimize resources and rates, and measure whether spend is generating business value. The FinOps Foundation organizes this around three phases: Inform, Optimize, and Operate — and the Crawl, Walk, Run maturity model maps directly to how organizations progress through them.

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.

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 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.

‍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.