Operations | Monitoring | ITSM | DevOps | Cloud

Software systems are getting better at understanding themselves. The mix of richer telemetry, smarter pipelines, and agentic AI is shifting observability from a passive record of events into something more active and useful. That shift is what we mean by context engineering. We recently partnered with O’Reilly on a report by David Beale that introduces the discipline. Before you read it, here are five things worth knowing.

AI has changed how software gets built. It hasn't changed how software gets run. Most of the AI money in software has gone into the IDE: code generation, copilots, developer assistants, faster pull requests. That work matters. But writing software is one slice of the lifecycle. The harder problem, and the more expensive one, is running that software in production. Production is where systems fail in ways nobody predicted. Incidents don't stay inside one service.

PagerDuty fires. You open the incident. Title, timestamp, nothing else. Whatever context exists is in someone's head, in a Slack thread from two weeks ago, or in a runbook nobody has touched since the last reorg. We got tired of that. So we put an AURA agent behind a Slack bot and pointed it at our own production environment.

By: Tucker Callaway, CEO at Mezmo The market keeps saying “SaaS is dead.” That’s probably true, but it’s also incomplete. What’s actually dying is the idea that value lives inside a vendor-controlled black box. The next era is about utilities: unlimited coding capacity and unlimited analytical capability. And if those two utilities are real, then the vendor model has to change.