Operations | Monitoring | ITSM | DevOps | Cloud

‍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.

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 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.

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.

When an employee leaves your organisation, a clock starts ticking. Microsoft begins deleting their data — OneDrive files, Exchange Online emails, Teams conversations — within days of their account being disabled. For most large enterprises this is happening continuously, quietly, and without IT teams necessarily knowing until someone asks for data that no longer exists.

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.