Operations | Monitoring | ITSM | DevOps | Cloud

Automation has always promised the same basic things: less work, faster resolution, and more consistency. What has changed is how much judgment automation can now apply before work begins. Traditional process automation is excellent at executing known steps. Agentic automation, though, adds a new layer: AI systems that can decide which action or workflow should happen next. That difference matters a whole lot.

Idle resources in your data center are a liability. Every unused server drawing power, every underutilized rack consuming cooling capacity, and every stranded port sitting dark on a switch represents money spent with no operational return. That's always been true for traditional compute. It's even more consequential as organizations invest in GPU infrastructure for AI workloads, where the hardware is an order of magnitude more expensive and the cost of underutilization scales accordingly.

The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value. In line with this shift, organisations are looking beyond where AI is trained to the specifics of where it is actually used.

As Staff Engineers and Principal Architects, most of us have spent years thinking about long-term system health. We are considerate of the company’s business objectives and strategy, accumulation of technical debt, and operational risk. For us it is not about whether code works today, but whether the engineer who inherits it in three years will be able to understand what it was trying to do and why. That's what makes a codebase maintainable rather than just functional.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.

Two-dimensional group-by improvements in Explorer.