Operations | Monitoring | ITSM | DevOps | Cloud

You've felt it. You're deep in a flow state with Claude or Cursor, building the next great thing, and then you hit the wall. Time to leave your editor, open a browser, click through a console, copy a connection string, paste it back, and pray you didn't fumble a character. The vibe is gone. What if your AI agent could just... do it? Deploy the database. Create the Kafka topic. Ship the app. All without you ever leaving the conversation. Today, that's real.

Engineering teams are shipping more AI-generated code than ever, but at what cost? Learn how to build a telemetry pipeline to monitor Claude Code usage and costs directly in SquaredUp. It is estimated that 85-90% of engineering teams are now using AI coding assistants such as Claude, Codex and Cursor. This is not just for small-scale pilot projects— around 40% of all code now being shipped is AI-generated, and in start-ups the figure is around 95%. This can result in incredible productivity gains.

AI models can reason over language, summarize findings, and explain patterns. What they cannot do on their own is see the real-time operational state of your environment. Ask a model about a critical incident and it will answer from whatever context it is given, which means the answer is only as trustworthy as the input. In operations and compliance workflows, an answer is only useful if it is grounded in current service context and governed access to the systems that define reality.

A majority of office professionals (72%) believe they understand how to use AI for their job better than the team responsible for managing AI at their company. While it’s encouraging to see employees embrace AI with such confidence, organizations will want to ensure they are providing the tools, guidance, and safeguards needed to help employees use AI safely.