Operations | Monitoring | ITSM | DevOps | Cloud

This is the fourth post in our DRIVE Deep Dive series. Over the coming weeks we're examining each pillar of the DRIVE framework in turn. For the complete model, download the full DRIVE framework. Our last post covered Initiatives. Up next: Efficiency. The bottleneck on writing code is gone, and the industry is responding the way it always does when a constraint disappears: by producing more.

AI is driving the next wave of digital transformation, but it is also creating an unexpected challenge for network operators: optical capacity is becoming a strategic bottleneck. The same AI boom fueling billions of dollars in data center investment is placing unprecedented demand on optical networking infrastructure.

NetBox is an open-source platform used for network infrastructure management and documentation, helping organizations track networks, devices, IP addresses, circuits, and racks. Sunbird’s new NetBox connector programmatically pulls device, port, and cabling data from NetBox into dcTrack using a preconfigured base XML connector, driving automation and giving you a single pane of glass and single source of truth across your entire infrastructure.

Last9 and Altinity now run observability entirely in your own cloud, metrics, logs, traces, and profiles on an open-source ClickHouse stack, priced on capacity instead of ingestion, with Altinity operating the database so your team doesn't have to. Last9 is an observability platform built for high-cardinality telemetry. It unifies logs, metrics, and traces with native OpenTelemetry and Prometheus support, real-time alerting, and long-term retention.

Ask any engineering leader how AI has changed their team, and you'll get an optimistic answer. Ask them to prove it with a number, and most go quiet. That gap is the story of 2026. Individual engineers are faster than they've ever been. Teams, in a lot of cases, aren't. We talked to our own engineering team, the people who've spent the last year building and shipping Upsun Dispatch and living through exactly this shift, and the same tension came up again and again.

Enterprise workflow automation can deliver faster resolution and ultimately more time back for IT teams. But not every workflow is ready to automate on day one, is it? This is where many automation programs stumble. Teams often start with signals like the most visible problem or the workflow that creates the most frustration. These are useful signals, but they’re not enough on their own. The best automation candidates are usually repeatable, measurable, and have clear inputs and outputs.

AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.

‍Teams building agents have converged on something that looks a lot like the software development lifecycle, but reshaped around a system whose output isn't deterministic: prototype an agent against a framework, evaluate it against a dataset of expected behavior, deploy it somewhere real, observe how it behaves against live traffic, and feed what you learn back into the next prototype. Call it the agent development lifecycle (Agent DLC).

‍ Here is a story platform engineering teams know by heart: developers find a shiny new tool, start building at a breakneck pace, and before you know it, the organization is drowning in a massive wave of unmanaged components. Right now, that exact story is playing out with generative AI.

As the size and complexity of their relational database workload grows, every company eventually goes through the process of off-loading work on a read replica. It comes with lots of benefits, but at a cost of increased complexity. This article is about how we dealt with that, a lot of learnings, and some useful techniques. incident.io is an incident management product relied on by thousands of customers to be the thing that supports them through anything from a minor blip to a full outage.