Operations | Monitoring | ITSM | DevOps | Cloud

Before you can answer whether checkout will survive Black Friday, you need a test you can rerun. In this demo, we used a coding agent and the Speedscale skills to record a Node app, mock its downstream API, and run a 30-second load test with 10 virtual users. That’s a small first step in holiday readiness. It gives you a working test and a result to inspect, with time left to fix what you find before a code freeze.

Dedicated vector databases have become such a standard part of the RAG conversation that teams often add one before they have proved they need it. According to studies, over 70% of companies using LLMs are using vector databases and RAG to customize their models. That shows how quickly the pattern has become normal. However, it does not mean every RAG application needs a separate retrieval system. If your application already runs on PostgreSQL, pgvector may be enough.

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.