Operations | Monitoring | ITSM | DevOps | Cloud

If they can turn it off, you don't own it - The AI kill switch problem

If someone else can turn it off, you don't own it. And most organisations haven't fully reckoned with what that means for their AI strategy. Civo Product Director Russ Smith draws a direct line from the Broadcom/VMware licensing shock to the Anthropic model restrictions, two different industries, same structural problem. When a vendor can change the rules overnight, businesses that built their strategy around that vendor are left with uncertainty and no clear next step.

AI isn't a black box. It's Pandora's Box.

When CFOs talk about AI budgets, they tend to describe it the same way: it’s a black box, offering little or no transparency. The bill arrives at the end of the month, it’s bigger than last month, and nobody can really explain why. Meanwhile, engineering keeps asking to raise the token budget. I think that framing undersells what’s actually happening out there. If the black box is the bill, the Pandora’s box is what you opened when you brought AI into the company.

Upgrade headaches? Extended Agent Support gives you breathing room #sysadmin #security #devops

The RHEL 7 agent deadline is here. Are you covered? Puppet Agent support for RHEL 7 is expiring soon—here is how to keep your systems secure. With RHEL 7 EOL approaching in August 2026, upgrading business-critical systems can be a massive headache. This overview explains how Puppet Extended Agent Support provides continued coverage for your legacy environments. Subscribe for more infrastructure management tips and leave a comment if you are planning a migration.

How to Build and Scale Unified Asset Intelligence for AI Success

Every IT leader has felt this tension: your organization has invested in AI, automation and digital operations, and yet outcomes still fall short of expectations. Even with the right tools and intent, you won’t be able to fully realize the value of your AI investments if they’re built on an unsteady foundation.

An Agent Is Only as Good as the Baseline It Reasons Against

Every vendor in networking has an agent story right now. The useful question for an operations leader is which of those agents can plan, act, and verify against a trustworthy model of the network, and which are assistants that retrieve and suggest, then leave the decision to a person. The direction of travel is settled.

Open Models Are Closing the Gap

The frontier models have led the pack for a while now. It seems like the big players of Anthropic and OpenAI keep leapfrogging each other by a couple points in benchmark scores every other month. But, a trend we are starting to see is that open weight models are improving by leaps and bounds. They don’t hold the lead and probably won’t for a while, but the fact that open models are scaring the leaders is something to think about.

Open Source AI Agent for SRE: Why AURA Is Free

The most common question since we started 31 Days of AURA: how do you plan to make money? The short answer is the control plane, not the agent. Mezmo sells an enterprise-grade control plane for running large numbers of agents across large environments, where coordinating across environments, governance, access control, and the efficiency of preprocessing MCP data start to matter. If a hundred people run AURA and three or four of them need that, the model works. The more people running AI agents in production, the bigger the market for the tooling underneath them.

The AI Technologies That Will Change Business Over the Next Five Years

Artificial intelligence is no longer viewed as an experimental technology reserved for large enterprises. It has become an essential business tool that helps organizations automate processes, analyze information, improve customer experiences, and make faster decisions. Over the next five years, AI will continue evolving beyond simple automation into systems capable of reasoning, collaborating, and independently managing increasingly complex workflows.

Enterprise Data Lineage for LoRA Policy Fleets

Enterprise reinforcement learning creates more than a training-data problem. It produces a chain of sensitive artifacts: task interactions, tool responses, evaluator judgments, rewards, checkpoints, LoRA weights, reports, and serving traces. When many policies share one foundation model, those artifacts may belong to different customers, workflows, or authorization boundaries even though they depend on the same base deployment.