Operations | Monitoring | ITSM | DevOps | Cloud

GenAI has moved well past the pilot stage. According to a Gartner finding, by 2026, more than 80% of enterprises will have used GenAI APIs or deployed GenAI-enabled applications in production. Today, GenAI is becoming an integral part of how infrastructure and application teams work every day. Organizations are depending on LLMs from a diverse range of vendors—OpenAI, Anthropic, Google AI, and DeepSeek—based on the strengths each offer for different use cases.

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.

n8n pricing runs €24 per month for 2,500 workflow executions (Starter), €60 for 10,000 (Pro), and €800 for 40,000 (Business), with 17 percent off on annual billing and custom Enterprise pricing above that. Every plan includes unlimited users and unlimited workflows. The self-hosted Community Edition is free with unlimited executions; you pay only for your server.

CoreWeave, a GPU cloud provider, prices start at $6.16 per GPU hour for an Nvidia H100 and reaches $8.60 for a B200, sold as fixed multi-GPU nodes: an 8x H100 node lists at $49.24 per hour on demand. Spot rates run up to 60 percent below on demand, reserved contracts discount up to 60 percent, and egress is free.

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

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?

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.

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.

Every developer knows the fatigue of the "12-tab code review dance": Agents have become first class citizens in SDLC and AI coding agents author code alongside human engineers, thus the above context switching destroys flow state. GitHub's gh CLI proved developers love the terminal, but modern delivery is tied to AI reviews, pipeline executions, risk scoring, and autonomous agents, not just git hosting.