Operations | Monitoring | ITSM | DevOps | Cloud

Every network engineer carries an instinct that outsiders mistake for caution: a change in one place can travel. Reroute a path, push a policy, drop an interface, and the effect can ripple across campus, data center, WAN, and cloud before the first alert is read. The blast radius of a network change is the reason operators move deliberately, and it is the single most important thing an AI agent takes on the moment it is allowed to act on the network instead of merely describe it.

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 teams rush to adopt AI, they often find themselves with conflicting workflows unique to each individual developer. To manage costs and promote good development practices, organizations need to establish Golden Paths around AI usage. AI Golden Paths are standardized flows that help developers work with agents more reliably and effectively. But how do you sift through all the possible workflows to decide what these Golden Paths should be?

AI is changing IT Operations, propelling faster incident investigation and event correlation to automated recommendations and remediation.

TLDR/: McKinsey’s new research on AI transformation, published August 28, 2026, studied 20 companies that have created real economic value from AI and found that only a small number have reached “Stage 3: Agentic AI enterprise.” The capability that separates Stage 3 from Stage 2, per McKinsey’s own maturity framework, is orchestration layers and automated guardrails: the ability to govern agent actions automatically, in real time, rather than reviewing them after the fact.

Open source software (OSS) offers organizations access to flexible, customizable, and often free software. It can reduce licensing costs and provide access to a large community of developers and users. However, open source does not automatically mean free, simple, secure, or risk-free. Organizations using OSS can face challenges around technical support, security, licensing, maintenance, functionality, and internal expertise.

If your Cypress suite has tests that fail more often or run slower, you know it can be hard to figure out the pattern from a single job. It could be one spec that slowed down, or a single test that fails, or maybe the entire suite is trending slower. The root cause could be a bug in the app, or a flaky test, or something else.

Microsoft Azure Monitor is the native monitoring platform for Azure environments. For teams already running workloads in Azure, it offers an obvious starting point: it's built in, deeply integrated, and parts of it are genuinely free.

The case for Cloud ERP has been settled for a while. The case for this year has not.

Raygun APM Agent 3.1 introduces more accurate asynchronous request tracing for Windows, Linux, and Azure App Service. Version 3.0 rebuilt the foundation of the Agent, profiler, installers, and release pipeline. Version 3.1 builds on that work with a focused improvement for ASP.NET Core: automatic request correlation that follows asynchronous execution without requiring developers to instrument their application.