Operations | Monitoring | ITSM | DevOps | Cloud

Shadow AI on company computers often starts with an AI agent that an employee installed on their own, signed in with a personal subscription and connected to internal systems through a Model Context Protocol (MCP) server. Shadow AI is one branch of shadow IT, with a shorter path from install to impact: these tools set up quickly, act on files and systems, and work under whatever account the employee chose. This guide skips the long definitions and goes to the practical part.

Most AI spend comes in with no tags and no owner attached. Your provider console shows total spend, maybe broken out by API key or model. It won’t tell you that the sales team spent $1,700 on Claude this week, let alone what the work was. And the problem is growing. McKinsey found that 56% of organizations now use AI in three or more business functions. More teams means more spend, and most companies respond with a spending cap. Set it too low and you slow down the work you wanted AI to help with.

Why is the telemetry AI agents generate so intimidating? If you turn on Claude Code’s internal telemetry it’ll throw a wall of text at you. And, it’s very expensive to store. But, the bigger issue is that you can’t make sense of it. Luckily it’s all OpenTelemetry native. That means you can configure it to send, transform, and store what you really need. Which raises the only question that matters. What do you actually need?

Western Canada is becoming a live test of whether sovereign AI capacity can be built responsibly at scale, and the answer will depend less on what operators promise than on what they can measure and show. Meta’s planned C$13-billion Alberta data center, BCE’s expansion of its Saskatchewan project to a 1.2-GW hub, and the federal Responsible Data Centre Development Principles all point to the same requirement: operational transparency that regulators, utilities, and communities can verify.

The "C" word is a controversial subject in the US, but we have to talk about "context", and what it means to an AI Agent. For starters, agents can only act on what's in their context window. Everything outside of it is a guess. In application code that limit is usually an annoyance.

Komodor spent years building an AI SRE platform before the category had a name. With the launch of the Komodor Agentic Operations Platform, it’s opening that engine up so enterprises can build, run, govern and optimize their own agents in production. Following the launch, co-founder and CEO Ben Ofiri sat down to talk about why now is the right time for agentic operations, what breaks between prototype and production, and where operations will head next.

TL;DR Most of the time we understand the tasks we're working on, but inevitably something comes up that we don't know much about, and we need just enough skill to cope. That used to mean reading paper documentation, then searching vendor sites and the web. Now it tends to mean a dialog with an LLM that has already ingested the documentation we don't have time to find and read. That's only half the problem, though.