Operations | Monitoring | ITSM | DevOps | Cloud

TLDR: Runtime Context is live, execution-level access to a running production system. It lets engineers and AI agents ask precise questions of running code and get answers immediately, without redeploying or interrupting users. This is the new baseline for reliability.

The AI sidekick called Dossinth I'm building is starting to take shape. So far, it became a moderately useful CRM, with a nice dashboard, and event log, daily briefing (which I am also receiving by email), account management and knowledge base. It's less AI and more CRM. For now. Here's a quick tour...

This article explains why AI-driven automation in incident response is feasible now. Teams can finally safely delegate repetitive and time-critical response tasks to AI Agents, which operate with contextual awareness and human oversight. The result is faster response, higher service uptime, and less alert noise – without losing control. ‍

In a recent post, I argued that AI doesn’t fix weak engineering processes; rather it amplifies them. Strong review practices, clear ownership, and solid fundamentals still matter just as much when code is AI-assisted as when it’s not. That post sparked a follow-up question in the comments that’s worth sitting with: With AI speeding things up, how do teams realise something’s gone wrong before users do? It’s the right question to ask next.

Welcome to January’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from vibe to prod. And this related news flash — AI spend keeps hitting new highs. pilots to production. In last month’s Pulse, we explored the compounding effect of AI becoming part of everyday cloud operations. This month, we see that pattern harden into year-end results.